EP3333799A1 - Device and method for determining energy consumption - Google Patents

Abstract

A power consumption determining apparatus (100) is provided comprising an energy input interface (107) for receiving an energy consumption statistic (103) and a parameter input interface (106) for receiving at least one external consumption parameter (105). Furthermore, the energy consumption determination device (100) has a computing device (101) and an output interface (108) for providing a power consumption prediction (104). The computing device (101) is set up to determine the energy consumption prediction (104) from the energy consumption statistics (103) and the at least one external consumption parameter (105).

Description

Field of the invention

The present invention relates to the technical field of power supply. More particularly, the present invention relates to a power consumption determination device and a method for determining power consumption.

Background of the invention

Since the opening up of the electricity market, there are more and more sellers of energy or electricity traders who buy electricity from suppliers and resell it to industrial customers or end consumers. Suppliers of the electricity may be electricity exchanges or the producers, e.g. Electricity or power plants. However, to be able to purchase the electricity and to be able to resell the electricity, however, contracts must be concluded. In particular, contracts with end users, especially commercial customers, can be costly because it is very difficult to predict what actual future consumption will look like. To be successful, the trader must rely on his gut feeling.

For private electricity customers, a flat-rate electricity mix is used, which is calculated on the basis of the average size of a household. In the professional field, that is, for large electricity consumers, commercial customers or industrial customers as final consumers, however, a good forecast of the purchase quantity is necessary in order to be able to create a competitive offer. In order to make such an offer, the different traders often use only their empirical values and estimates to make predictions about future power requirements. However, these heuristic approaches can lead to very high oversupply and undersupply at certain times.

Summary of the invention

It may be desirable to enable efficient determination of power consumption.

According to one aspect of the present invention, there is provided a power consumption determining apparatus and method for determining power consumption, in particular, for predicting power consumption.

The invention is indicated by the features of the subject matters of the independent claims. Embodiments and further aspects of the invention are indicated by the subject matters of the dependent claims and the following description.

In the context of this text, the term "amount of electricity" and "amount of energy" may be used equivalently. In essence, both terms may be understood as the energy to be delivered.

According to one aspect of the invention, there is provided a power consumption determining apparatus comprising an energy input interface, a parameter input interface, an output interface and a computing device, a processor or a microcomputer. The energy consumption determination device is configured to receive energy consumption statistics, such as a load curve or load profile. The parameter input interface is configured to receive at least one external consumption parameter, and the computing device is configured to determine an energy consumption prediction from the energy consumption statistic and the at least one external consumption parameter, and the energy consumption prediction at the Provide output interface. In one example, the energy consumption prediction may be provided as a list of daily consumptions and / or consumption values distributed over the year. In another example, the energy consumption prediction may be provided as an offer in which the expected energy consumption and / or energy consumption values are priced. The external consumption parameters may be stock market data of an external power exchange, a sunshine statistics and / or a wind statistics. It can be any type of parameter used to determine an energy usage prediction, such as postal codes and / or calendars.

According to another aspect of the present invention, a method of determining power consumption is described. The method comprises receiving an energy consumption statistic, receiving at least one external consumption parameter, determining the energy consumption prediction from the energy consumption statistic and the at least one external consumption parameter, and providing the energy consumption prediction or energy consumption prediction statistics.

For example, the energy consumption statistics can be evaluated and by evaluating the energy consumption statistics, an end user can be identified. Additionally or alternatively to the identification of an end user, a manipulated energy consumption statistic can be detected by evaluating the energy consumption statistics. So it may be possible to prevent unnecessary energy consumption forecasts from being created. Or manipulation attempts can also be detected before the energy consumption prediction is determined or determined. An unnecessary determination of an energy consumption prediction from the energy consumption statistics and the at least one external consumption parameter can thus be prevented.

According to another aspect of the present invention, there is provided a program element having program code which, when executed by a processor and / or by the controller, performs the method of determining power consumption.

According to another aspect of the present invention, there is provided a computer-readable storage medium having stored therein a program code which, when downloaded from a processor and / or the controller is executed, the method for determining a power consumption performs.

As a computer-readable storage medium, a floppy disc, a hard disk, a USB (Universal Serial Bus) storage device, a RAM (Random Access Memory), a ROM (Read Only Memory) or an EPROM (Erasable Programmable Read Only Memory) may be used. The storage medium can also be an ASIC (application-specific integrated circuit) or an FPGA (field-programmable gate array), as well as an SSD (solid state drive) technology or a flash-based storage medium. Likewise, a web server or a cloud can be used as the storage medium. As a computer-readable storage medium may also be considered a communication network, such as the Internet, which may allow the downloading of a program code. A wireless network technology and / or a wired network technology may be used.

To speed up the creation of a power supply, a server may be provided that allows prospects to contact the server through their client computer or client software such as a browser. For contacting the server, the power input interface may be implemented as a communication interface. A communication interface may operate in accordance with a communication protocol and be connected to a transport device, such as a communications network. Via this energy input interface, an energy statistics, for example a load profile or a load curve, can be forwarded to the energy consumption determination device. A load profile can be provided as a curve and / or as an ordered list, for example as a list in Excel® Formal, Microsoft® Office® format, XLS (X) format, XML (Extensible Markup Language) format, OpenXLS Format, in CSV (comma-separated values) format or in any other standard format for energy statistics. The ordered list may essentially have at least two columns, one column having a date value and the other column having an energy value corresponding to the date value. The date value can be displayed in a format according to DIN ISO 8601. In one example, the date value includes a day of a year and a time.

The power input interface may be specified according to an interface description. In the interface description, the functionality of the interface, but also a corresponding protocol can be specified. The energy input can be configured such that a user, a terminal, an application (App) and / or a client load profile as at least one load profile file and / or as a plurality of load profile files in any file format via an interface to the computing device Energy meter can provide and / or upload. For providing the energy input interface may have as a transmission protocol at least one protocol selected from the group of transmission protocols consisting of a TCP / IP (Transmission Control Protocol / Internet Protocol) protocol, a UDP (User Datagram Protocol), a http (Hypertext Transfer Protocol) Protocol, a FTP (File Transfer Protocol) protocol, a WebDAV (Web-based Distributed Authoring and Versioning) protocol, an ActiveSync protocol, an SSH (Secure Shell) protocol, a CalDAV (Calender Distributed Authoring and Versioning) protocol, a vCard Protocol, an iCalendar protocol and an iCal protocol. The interface of the computing device may be designed so that it reads in the energy consumption statistics, the load profile or the load profile files. The computing device or a program that controls the computing device may also be set up to convert this read-in energy consumption statistics or the load profile file into a standard format. The conversion to a standard format may provide that additional information of the user is added to the user-provided energy consumption statistics that are required for a quotation. For example, the added data may include information that identifies a user and / or a client. This data may include address data of the user, such as place of residence, street and house number, but also address information of the client, such as its network address and / or its IP (Internet Protocol) address.

The computing device may still have an internal interface to which the data is passed to the standard format after conversion. The power input interface sends its data to this internal interface. This internal interface can lead to a Rechenunit the computing device. By means of the Rechenunit or CPU (Central Processing Unit) the different collected data can be summarized and evaluated. For storing the acquired data, which may also have, for example, external consumption parameters, which the CPU via the Parameter input interface are provided, the computing device and in particular the CPU may be connected to a database. All end user specific or general data can be stored in this database. For example, industry-standard energy consumption statistics or industry-standard load profiles can be stored in this database. It may also be stored there, for example, a total energy consumption forecast or a total energy consumption statistics. Predictable calculation algorithms can be used to generate an energy consumption prediction and / or an offer for energy consumption. Thus, calculations can be performed with the stored data and an energy consumption prediction can be calculated with the data. The energy consumption prediction may also be provided as a file and / or ordered list that is essentially constructed like the energy usage statistics. In this list, sorted calculated energy consumption values can be collected according to date values and / or time values. The energy consumption values may be given in a physical consumption unit, such as the unit watts (W), watt-hours (Wh) or the unit mega watt-hours (MWh) or in a multiple thereof. However, energy consumption prediction may also be a total value of energy consumption related to a time value, such as one year. From the energy consumption statistics can be created by the computing device in the event that a price indication is provided as an external consumption parameter, an offer for a specific load profile, for a specific energy consumption statistics, for a specific client and / or for a specific end customer. An offer can therefore be a monetary physical variable. The energy consumption statistics, the energy consumption forecast and / or the offer can be returned to an end user and / or client via the output interface. The created offer is sent to the user via this interface. The power input interface and the output interface may be implemented as a common bidirectional interface. The power output interface may use the same protocol as the power input interface. This protocol may be implemented as a client / server protocol or as a command / response protocol.

In one example, the energy usage prediction may be used to control a customer's own power supply. A customer of, for example, his own wind turbine and / or solar system as a separate power supply device can see from the provided energy consumption forecast when it pays off for him to store self-generated electricity because, for example, the electricity purchased by an energy trader is more favorable. The energy consumption prediction and, in particular, an ordered list developed therefrom as a program can be used as a control program for the energy supply device.

Through the parameter input interface, the energy usage determination device may request various external sources to still obtain data needed to calculate an energy usage prediction. For example, data may be queried that is missing in the internal database of the computing device and / or outdated. In particular, data may also be requested that are necessary for the creation of a consumption offer. These data and / or parameters may be, for example, purchase prices for an amount of energy. These prices can result from supply and demand and be time-dependent. For example, these may be prices for a quantity of electricity. However, it can also be a decrease statistics, which indicates at what time, for example, at what date and at what time, which amount of electricity and / or amount of energy is provided by a provider. However, the consumption parameters can also be a postal code statistic which indicates the geographical longitude and / or latitude of a location with a specific postal code, a holiday calendar which indicates when a particular federal state has a holiday and where applicable Bridges are lying to a wind statistics and / or a sunshine statistics. In essence, the external consumption parameters may be information provided as an ordered list that indirectly or directly impacts energy consumption. Such external consumption parameters can already exist as an ordered list in a predefinable and further-processable format, such as the energy consumption statistics. However, they can also be in a different format so that the computing device first extracts the data required for the energy consumption prediction from the received external consumption parameters in order to arrive at an ordered list. For extracting the required information, the computing device can use data mining functions for a database. For example, the temporal course of the stock exchange prices can be analyzed and compared with the energy consumption statistics in order to arrive at a risk assessment. If, for example, high volatility of stock market prices is detected, there is a high level of risk.

Anomalies can be detected by examining a load profile or energy consumption statistics. Anomalies in the load profile may indicate tampering. Such manipulations also correspond to a risk. By detecting anomalies, this risk can be identified and taken into account in the quote calculation.

But the load profile or the energy consumption statistics also makes it possible to detect whether work has been carried out on bank holidays and / or potential bridge days. From such observations, conclusions can be drawn for the future.

By means of the consumption parameters, in particular by means of the external consumption parameters, which have to be procured from sources other than the internal database, the necessary framework conditions for the energy supply can be determined. In this determination of the framework, essentially, the energy consumption statistics provided by an individual end user may be set in the ratio of the total energy consumption forecast managed by the computing device. The computing device may, in one example, manage the overall energy consumption prediction of a electricity trader. In other words, in determining a customer-specific or client-specific individual energy consumption prediction, the individual energy consumption statistics may be placed in the ratio of the total energy consumption prediction or the total energy consumption statistic to produce a quality measure for the individual energy consumption statistics and / or for the individual energy consumption prediction. The quality measure may be a price for an offer that determines the ratio of the individual energy consumption prediction to the total energy consumption prediction. The quality measure may be a correlation measure between a provided energy consumption statistic and a total energy consumption prediction. The quality measure can be used as a factor that increases an offer price if the provided energy consumption statistic fits badly with the overall energy consumption prediction or lowers an offer price if the provided energy consumption statistic fits in well with the overall energy consumption prediction. By combining the consumption parameters and / or the procurement costs for the energy and the procurement packages for the energy, it is possible to determine which amount of energy must be purchased. Here, economic as well as physical factors can be taken into account.

For example, from the combination of consumption parameters, further conclusions can be drawn on the energy consumption statistics. Thus, the obtained energy consumption statistics can be correlated with externally obtained and / or internally available sunshine statistics. In this way, on the one hand, it can be found out whether the production site underlying the energy consumption statistics provided is a production site which uses its own energy generation plant, for example its own solar plant and / or wind power plant. Such a solar system or photovoltaic system can ensure with a correspondingly strong solar radiation that it provides itself for the energy supply and thus takes place in a certain time no energy purchase from the electricity trader, although this was provided in a decline forecast. In one example, it can also be taken into account that the own energy supply system stores the energy in order to use a low electricity supplier's electricity and to use the self-generated stored electricity, if electricity can only be bought at a high price. For the consideration of a storage, the computing device may resort to indications of the memory size of the memory, for example a battery, and the maximum supply duration from such a memory as an external consumption parameter. In another example, however, the own energy supply system can be included in the energy consumption prediction, so that the energy consumption prediction also has a control program for such a separate power supply system. Likewise, an energy storage device of a customer can be included in the energy consumption prediction. A separate storage device, which is made available to a power trader for use, for example, can be included in the determination of the energy consumption prediction with a favorable quality parameter. Due to the consideration of the external consumption parameters, predictions or energy consumption forecasts can be created on the basis of recognized functionalities, such as, for example, own energy production facilities or own storage facilities, which deviate only slightly and / or only to a small extent from the actual values.

The automatic comparison of the energy consumption statistics and the consumption parameters can very quickly make an offer for an energy supply, in particular be created for a power delivery. In realizing the energy consumption determining devices as a server system, in an on-line dialogue between a client system served by a potential customer and a server system, a 24-hour automatic energy consumption prediction and / or offer may be provided for individual consumption characteristics or created for individual energy consumption statistics of a customer. In particular, this customer can be an industrial customer in which only very inaccurate offers can be created with a standard load profile. The energy consumption forecast can be created in different forms. For example, the energy consumption prediction corresponding to the energy consumption statistic may be provided as an ordered list. In this list, the date value can be encoded in a format according to DIN ISO 8601.

To prepare an offer, the potential customer may upload via his client an energy consumption statistic, for example a load profile, to the server via a dedicated energy input interface. The server can query the corresponding external consumption parameters from an internal and / or external database immediately after the upload based on the information provided in the energy consumption statistics and, together with the load profile, generate an energy consumption prediction in the form of an offer.

A large number of contracts can thus be concluded and it can be checked with each new customer whether the customer matches the dealer profile and / or the overall energy consumption forecast. A trader's profile may be understood to mean an acceptance forecast that a trader has to make in order to estimate at what point in time electricity must be purchased in order to meet a demand that is needed, without having to buy expensive electricity at short notice. It may be avoidable that a high number of end consumers, in particular of industrial end users, leads to the loss of the overview of the amount of energy available at a given time. It may also be possible for the different considerations that need to be made to provide a prediction to be rapid, and that making an offer for a professional power customer may not take several days. The prediction can be made with the help of physical parameters and it is no longer necessary to rely on a gut feeling. According to another aspect of the present invention, the at least one consumption parameter is at least one parameter selected from the group of parameters, the group of parameters consisting of energy cost, sunshine statistics, wind statistics, industry-specific load history, and a calendar.

The energy costs may include purchase prices and / or energy amounts of power exchanges. These subscription prices can be offered in different profiles and / or packages. The sunshine statistics can provide information as to when possibly a customer's own power generation plant provides power, such as a solar system. During the period in which the power generation plant supplies power, there is essentially no power take-off or only a reduced power take-off from a power trader. Or it takes place despite its own energy production, a current decrease, since just energy is available at low cost. Likewise, a wind statistics can provide information about when a customer's own wind turbine generates energy. Furthermore, a calendar, in particular a public holiday calendar, can provide information about the times when less power is expected to be consumed, since, for example, in a certain federal state is a public holiday. Also, bridge days can be found in a calendar to get an indication of low energy consumption. In one example, it can be detected by means of an energy consumption statistic or a load profile, if work is being done on a holiday and / or a bridging day and energy is consumed. For example, it can be deduced from this knowledge that energy consumption is expected to continue on such a characteristic day in the future. In another example, when power is removed during sunshine and in the presence of a solar system, a potential memory may be inferred. An estimate of the memory size can also be made from the duration of the current decrease, and it can be determined that a customer is using such a memory even though he did not specify it. It is also possible to recognize individual behaviors and to deduce from them what times are worked and at what times they are not worked. To locate the state, for example, the postcode of a customer can be used. Essentially, the computing device may be set up to recognize characteristic patterns for typical behavior and / or usage in an energy consumption statistic. For this purpose, all methods of Image processing, pattern recognition, statistics, artificial intelligence and / or graph analysis. In one example, the patterns are dependent on physical quantities, such as power consumption and / or power consumption.

The consumption parameters, such as the consumption statistics of a separate energy supply, the sunshine statistics or the wind statistics can also allow to detect at all whether a customer operates a solar system or to a wind turbine, for example, a provided energy consumption statistics or a load profile with corresponding parameters of the solar and / or wind statistics is correlated. From the energy consumption statistics may also be derived, for example, whether regular consumers are turned on at certain times. This can be, for example, energy-intensive consumers such as agitators, but also a cleaning team, which is active at certain times and consumers such as lights or cleaning machines.

According to another aspect of the present invention, the power input interface and / or the output interface are / are arranged as interfaces that enable communication with a client.

For the communication between a client and a server, a communication interface may be provided which uses, for example, the http (Hypertext Transfer Protocol) protocol. The provision of a communication interface may allow the energy usage determination device to operate as a server that can be serviced with one or more clients.

It is also possible to access a server by means of a smartphone application or app by providing standard communication protocols. For access via a mobile terminal, such as a smartphone and / or a corresponding app, the computing device may recognize that the energy consumption determination device is accessed from a mobile terminal and provide a user interface with a reduced graphics. The server may provide web pages in a responsive web design, the server recognizing which terminal the server is being accessed by, and adapting the format of the pages provided to the capabilities of the terminal. The graphic structure of a "responsive" website is based on the requirements of the particular device with which the site is viewed. This applies in particular to the arrangement and display of individual elements, such as navigations, page columns and texts, but also the use of different input methods of the mouse (click, drive over) or touch screen (type, wipe). The technical basis for this are, for example, web standards HTML5, CSS3 (hypertext markup language / cascading style sheets, in particular the media queries) and JavaScript.

According to a further aspect of the present invention, the computing device is set up to identify an end user by evaluating the energy consumption statistics and / or to recognize a manipulated energy consumption statistic.

An end customer can be identified and / or identified on the basis of his energy consumption statistics. An energy consumption statistic may essentially be an individual characteristic for a particular customer, in particular for the plant operated by him, for which an energy prediction is to be made. Thus, an energy consumption statistic can be considered as a fingerprint, which makes it possible to recognize a certain end customer or a certain consumption statistic. It may be possible by recognizing a consumption statistic or a load profile to recognize when a customer repeatedly tries to provide the identical or a similar energy consumption statistics, for example, to obtain reference prices or to determine price changes at regular intervals. It may also be possible to detect tampering with energy consumption statistics when analyzing energy consumption statistics. For example, in order to obtain a low-cost offer, times when there is a high level of consumption, a customer may wish to delete from the energy consumption statistics to provide energy consumption statistics that closely match the electricity trader's overall energy consumption statistics. In particular, a manipulation may have been made that high consumption values have been shifted to periods of discounted energy prices in order to maintain overall consumption but to suppress the bid price. Such manipulations may be undertaken by a customer to obtain a favorable offer for artificial consumption prediction. Manipulation may include, for example, the deletion of peak load areas or decrease peaks, ie areas with maximum energy consumption. By means of the computing device and a correlation With external consumption parameters and with the total energy consumption statistics and / or with the information stored in the internal database or the external consumption parameters, such manipulations can also be evaluated and recognized. The detection of a manipulation can lead to a rejection of the potential customer or to a correction of the manipulated energy consumption statistics in order to generate a consumption prediction with a corrected consumption statistics and to create an offer based thereon.

It may also be possible, through the analysis of the energy consumption statistics, to recognize that a potential competitor is making a large number of standard consumption requests to the energy consumption determination device. In recognizing such requests, which are usually made to obtain competitive offers, the computing device can prevent the provision of a consumption forecast. In addition, for the future avoidance of such requests, the client-specific information may be stored, for example, a source address or an IP source address.

In another example, it may be possible to use the provided energy usage statistics to identify an anonymous end customer. Namely, a power consumption prediction can also be made by a potential customer, who does not have his data, which he must state in order to be able to identify himself, or who does not want to specify this data. For example, in such a case, an individual server-specific token may be stored on the server side. This token may be stored on the server and associated with a particular energy usage statistic. If the logon process is interrupted, the interrupted logon process can be restarted at the point at which it was interrupted by means of this token. By means of this token, however, potential price inquiry machines can also be recognized, in particular if the token is combined with the energy consumption statistics as a fingerprint. Thus, a possible misuse of the energy consumption prediction may be detected on the server side and avoided. It may also be possible to detect and block a switch of a system for determining power consumption when it is not desirable for a price search engine to send requests on behalf of an end customer. In one example, the token may be an individual code or marker or an individual Number for a specific request with which a request can be uniquely identified.

According to another aspect of the present invention, the power input interface is configured to generate energy consumption statistics.

For example, an energy input interface may have an input mask configured as a menu that allows a customer to generate a rough energy consumption statistic if the customer does not yet have their own energy usage statistics. The presentation of the menu may be related to energy consumption. For the creation of the artificial energy consumption statistics, which has rough indications of the customer's energy consumption, the input interface may provide virtual slide switches and / or virtual controllers to provide even a rough energy consumption statistics. By means of the virtual slide switches and / or regulators, the customer can compile an expected energy consumption curve or energy consumption statistics manually. For example, this statistic may include the days of a week and prehistoric times when a given usage is expected. From this information, an approximate energy consumption prediction can be compiled by creating an energy consumption statistic with the specified energy consumption. This artificial energy consumption statistics may be represented as a square wave. To well match this rough rectangle curve to a real consumption, a base consumption can be added as a constant and / or the rectangular curve can be smoothed using stored other energy consumption statistics. The basic consumption constant may, in one example, be calculated as a percentage of the total annual energy consumption and / or the daily energy consumption. In a further example, a branch-typical load profile can be determined from a large number of load courses of companies of a specific branch, for example of printers. To create the industry-typical load profile, the large number of load profiles from companies in a common industry can be read from a database. In order to smooth in particular the rectangular shape of the start of work and the working end, a soft curve rise and curve descent can be interpolated from the large number of load gears. The function obtained by the interpolation may be used as a generated load profile to generate consumption forecasts.

According to another aspect of the present invention, the energy consumption determination device further comprises a trade interface, wherein the trade interface is configured to purchase and / or sell energy.

By means of a trade interface, the energy consumption determination device can access a power exchange immediately after a power consumption prediction has been made and / or if, for example, a potential customer has signed a contract to purchase the required energy. Purchasing can cover the anticipated energy consumption determined by the energy consumption forecast using supply contracts. The purchase of energy may, in essence, be automatic in order to cover the prices prevailing on the electricity exchange at the moment of determining the consumption forecast. In one example, however, it may also be possible to prepare a power purchase and, for example, to provide a completed form that merely has to be forwarded to a power exchange in order to initiate the purchase process. In another example, it may be possible to delay shopping by the computing device until a favorable price for a power supply package on the power exchange prevails.

According to another aspect of the invention, the computing device is set up to design a presentation menu in dependence on the energy consumption statistics.

In order to produce an energy consumption statistic, the energy consumption determination device can offer a menu that is shaped, for example, depending on the energy consumption statistics provided. Thus, for example, a particular attractive offer can be displayed if, on the basis of the energy statistics, it is recognized that a potential customer and / or his energy consumption statistics on average fits well with the portfolio and / or the seller's overall energy forecast statistics. In particular, a good deal for a good buyout may fit on a power exchange. In other words, an offer may be considered good if, through the associated energy consumption statistics, the total energy consumption statistics of the electricity trader can be well covered or approximated with favorable energy packages of a power exchange. A customer considered to be attractive can be offered particularly favorable terms or favorable termination conditions. The offer menu can thus be designed depending on the energy consumption statistics provided. Thus it can be achieved that a customer chooses an offer that ensures a good utilization of the electricity trader. For example, a customer who consumes power at times when a large number of other customers are not powering may appear as an attractive customer to ensure good utilization of the power collection.

According to another aspect of the present invention, the energy consumption determination device has a time measurement device or a timer, wherein the time measurement device is configured to delete the validity of a energy consumption prediction and / or a created offer after a predeterminable time.

A generated energy consumption forecast may be provided as an offer. Due to the rapid availability of the offer, it may be desirable to also make a quick commitment to the offer, for example, to secure the prices underlying the offer or the energy forecast on a power exchange. A quick commitment may also be desired to ensure that a potentially good customer still fits so well with the underlying overall energy usage statistics of the electricity trader. In particular, it may be ensured by means of a limited-time offer that the desired customer, who for example can fill in gaps in a total energy consumption prediction by his energy consumption statistics, can be obtained quickly as a customer. For if a customer decides to become a customer of the electricity supplier only after a large number of days, the inclusion of additional customers could have already created another energy pattern for the total energy consumption statistics or the total energy forecast, in which gaps are already covered could well have been filled in by the energy consumption statistics of the potential customer. If a contract has already been signed with other customers, a previously attractive customer may be less attractive. However, if the offer is only made available for a limited time, it can respond quickly to the needs.

According to another aspect of the present invention, a system for determining power consumption is provided. This system comprises a switch and a plurality of power consumption determining devices. The switch is connected to the plurality of power consumption determining devices and the switch is arranged to poll a plurality of energy consumption forecasts from the plurality of energy consumption determination devices and to generate a single energy consumption prediction therefrom. For this query, the system can obtain a power-consumption statistic from a client and distribute it to the various energy-use-determining devices.

Such a system may allow a customer to access only a single system to request a variety of different electricity traders for energy usage forecasts, quotations, and / or energy supply contracts. This switch may use the provided power input interfaces of the various power consumption determining devices to receive and pass on energy consumption statistics to a plurality of portal systems or energy consumption determining devices of the different electricity providers. In this case, the switching device can pretend to be a client for a power consumption determination device with respect to the energy consumption determination devices. For example, the system for determining the energy consumption statistics can be realized as a robot or robot. Such a robot may query a plurality of energy consumption determination devices substantially simultaneously. A robot may be a program programmed to perform automatically predefinable queries on a computer network and collect data.

In turn, the energy usage determination device may be configured to recognize, based on the provided energy usage statistics and / or the behavior of the switch, that it is being addressed by an artificial client and disabling that artificial client for further access. To recognize the energy consumption statistics can be used again as a fingerprint and / or as a license plate. Statistical methods can also be used to detect manipulations of energy consumption statistics and in this way certain clients can be blocked either permanently or for a certain period of time. To lock clients, the computing device may run a blacklist or blacklist that lists the clients identified as robots to ignore their requests.

It should be noted that different aspects of the invention have been described with reference to different items. In particular, some aspects have been described with respect to device claims, while other aspects relating to method claims have been described. However, one skilled in the art will appreciate from the foregoing description and the following description that, unless otherwise stated, in addition to any combination of features belonging to a category of objects, any combination of features will also be disclosed as being of this text refers to different categories of objects. In particular, combinations between features of device claims and features of method claims should be disclosed.

Brief description of the figures

• Fig. 1 zeigt ein Blockdiagramm einer Energieverbrauchbestimmungsvorrichtung gemäß einem exemplarischen Ausführungsbeispiel der vorliegenden Erfindung.
• Fig. 2 zeigt ein System zur Bestimmung eines Energieverbrauchs gemäß einem exemplarischen Ausführungsbeispiel der vorliegenden Erfindung.
• Fig. 3 zeigt eine Energieverbrauchsstatistik gemäß einem exemplarischen Ausführungsbeispiel der vorliegenden Erfindung.
• Fig. 4 zeigt einen Ablaufplan für das Erstellen eines künstlichen Lastgangs gemäß einem exemplarischen Ausführungsbeispiel der vorliegenden Erfindung.
• Fig. 5 zeigt eine Gesamt-Energieverbrauchsvorhersagekurve und eine GesamtEnergieversorgungskurve gemäß einem exemplarischen Ausführungsbeispiel der vorliegenden Erfindung.
• Fig. 6 zeigt ein Flussdiagramm für ein Verfahren zum Bestimmen eines Energieverbrauchs gemäß einem exemplarischen Ausführungsbeispiel der vorliegenden Erfindung.

In the following, further exemplary embodiments of the present invention will be described with reference to the figures.

• Fig. 1 FIG. 12 is a block diagram of a power consumption determination apparatus according to an exemplary embodiment of the present invention. FIG.
• Fig. 2 shows a system for determining an energy consumption according to an exemplary embodiment of the present invention.
• Fig. 3 FIG. 12 shows energy consumption statistics according to an exemplary embodiment of the present invention. FIG.
• Fig. 4 FIG. 10 is a flowchart for creating an artificial load profile according to an exemplary embodiment of the present invention. FIG.
• Fig. 5 FIG. 12 shows an overall energy consumption prediction curve and a total energy supply curve according to an exemplary embodiment of the present invention. FIG.
• Fig. 6 FIG. 12 is a flowchart for a method of determining power consumption according to an exemplary embodiment of the present invention. FIG.

Detailed description of embodiments

The illustrations in the figures are schematic and not to scale. In the following description of the Fig. 1 to Fig. 6 the same reference numbers are used for the same or corresponding elements.

Fig. 1 FIG. 12 shows a block diagram of a power consumption determination device 100 according to an exemplary embodiment of the present invention. The power consumption determination device 100 includes the computing device 101 and is configured as a server 100. This server 100 may be addressed by a client 102 via the power input interface 107. The energy consumption statistics 103, for example a load cycle 103, can be provided or uploaded to the computing device 101 via this interface 107. For example, energy consumption statistics can be provided in XML, XLS, XLS (X), or CSV format. The client 102 may contact the computing device 101 via an http protocol. For example, the client 102 can reach the server 100, in particular the computing device 101, via a communications network, for example via the Internet. The computing device 101 may include a web server, which offers a client 102 different web pages for entering the data. Thus, an input mask can be provided, which allows the uploading of energy consumption statistics 103.

As a result of entering the energy consumption statistics, the computing device 101 may provide a power consumption prediction 104. The energy consumption prediction 104 may provide the predictive data of daily usage as predictive values in the form of an ordered list in either an XML format, an XLS format, an XLS (X) format, or a CSV format. However, this consumption forecast 104 may also be provided in the form of a rate-of-use energy consumption prediction or energy consumption prediction statistics. This forecast provides an estimation of the costs in the form of an offer expected for a scheduled energy decrease. The Energy consumption prediction is provided via the output interface 108. The output interface 108 may be a communication interface that also uses the http protocol. The computing device 101 or the web server 101 can contact the client 102 via the http protocol and / or the IP protocol. Physically, an Internet can be used between the client 102 and the computing device 101.

In order to determine an energy consumption prediction 104 from the energy consumption statistics 103 provided, the load profile 103 or the load curve 103, the arithmetic unit 101 can query at least one or a plurality of external consumption parameters 105 via the parameter input interfaces 106. The external consumption parameters can be, for example, current electricity prices and / or energy prices of a power exchange and / or energy exchange, for example the EEX (European Energy Exchange) power exchange. Other examples of external consumption parameters 105 may be solar irradiation statistics or wind statistics. The sunshine statistics and / or the wind statistics may be given as a function of a postal code or a geographic coordinate and may have the prevailing / prevailing solar radiation and / or wind strength and direction at a certain time at a particular time. About the solar radiation statistics or wind statistics can be determined at what time energy is supplied, for example, from a solar power plant of a customer or a wind farm of a customer. In another example, the delivery quantity and / or delivery time of a bio-power plant can be determined. The potential customer uses the client 102 to contact the energy consumption determination device 100, in particular the computing device 101. A holiday calendar 105 may also be provided as an external parameter. From such a holiday calendar can be derived on which days a holiday falls and how the energy consumption statistics of holidays depends. This dependency on holidays can be used for energy consumption forecasting.

The computing device 101 may offer an anonymous or password-based access. For example, an anonymous or anonymous access is enabled if a potential customer 102 only wants to retrieve some basic information. It may be provided that in the case of an anonymous access, the computing device 101 only makes rough statements about an energy consumption prediction or produces a corresponding rough offer, which is created by using fewer analyzes be carried out as a binding offer. Thus, depending on the selected access a different detailed offer can be created.

In a personalized offer, however, the potential customer 102 already gives his specific data. The computing device 101 can create a token or a server-side cookie as a server-side device for a personalized access. In the token and / or server-side cookie, customer data for a specific request and / or for a specific energy consumption statistics can be stored. The server-side storage of the customer information can be used to allow the recognition of a potential customer, if he wants to interrupt the query process and would like to resume it later. The continuation of an offer creation and / or an energy usage prediction may be made by specifying an individual operation number or the token provided by the server. Also, the client used by the user can be recognized. It may be possible to continue without the same client being used. For the client-independent continuation of the energy consumption statistics can be used as an individual identifier. Here, the cookie is not stored on the client but on the server, so that the continuation of the process can be done regardless of which client it is performed. Thus, a client-independent login on the computing device 101 is possible.

However, since it is possible that when interrogating an electricity supplier who operates the portal 101, a computing device 101 generates a fingerprint of these statistics 103 when evaluating the provided energy consumption statistics 103, the computing device can find out whether the same load cycle 103 is used repeatedly, for example However, the computing device 101 can also find out whether it is a natural load profile 103 or a manipulated load profile. For example, a potential customer may have cleared a high-peak-time consumption in the load path 103. Due to plausibility considerations, the computing device 101 can find out such manipulations.

The plausibility considerations can be implemented in the computing device as plausibility tests. For example, the plausibility tests can be edge checking exhibit. A load can be an increase in the load curve. The increase is characteristic of every consumer in the business. Various of these characteristic curves of a consumer may differ from each other according to a statistical quantity having a statistical distribution. Also, on working days, Saturdays, Sundays and public holidays can result in statistical deviations of the characteristic course. Deviations can be detected with threshold values. However, basic patterns can be recognized in the load profile. For example, by eliminating statistical deviations.

Another plausibility test is the comparison of the load peak distribution in relation to different times of day and / or times with each other. Furthermore, a time comparison of the start of operation and / or the start of work can be used on different days for plausibility control. For example, at 7:30 a rise in a load curve can be detected by means of an edge, and at 9:00 a change in the load curve can be detected when another edge occurs. At the regular positions of the flanks, the regular operation can be detected. The flanks can be recognized, for example, by the formation of the derivative of the load curve or by means of a Fourier transformation of the load profile curve.

For example, a fingerprint of energy consumption statistics is recognized by the fact that certain anomalies are found. For example, at certain times a stirrer or other consumer might turn on regularly, resulting in an individual pattern of a particular customer. Likewise, at regular intervals, a cleaning crew could become active in a company which, during its activity, turns on consumers such as vacuum cleaners or light sources. Such anomalies can be used to detect an individual load profile. The anomalies may also indicate manipulations that pose a risk in predicting energy consumption. Anomalies may also be identified by comparison with a typical industry-typical load curve and possibly synthesized load curve for the investigating company. An industry-standard load curve can be determined from a variety of load curves of companies in a common industry, for example by interpolation. If too large a deviation of a load curve to be evaluated compared to an industry-standard load curve and / or compared to an industry-standard synthesized Load curve is present, can be closed to possible manipulation and the load curve to be evaluated can be examined in more detail.

In order to determine an offer 104 or an energy consumption prediction 104 from an existing energy consumption statistic 103, the arithmetic unit 101 can resort to the external consumption parameters 105 in order to carry out a calculation. The result of the calculation can be provided via the interface 108 in the form of an offer 104.

Fig. 2 shows a system 200 for determining power consumption, according to an exemplary embodiment of the present invention. This system 200 has a switching device 201. This switching device 201 can be embodied as a portal, for example as a price comparison portal, and can also be operated as a server 201. This server 201 can be queried by a client 203, for example a customer, via a communications network 202, for example via the Internet 202. An HTTP protocol can be used between the client 203 and the server 201 in order, on the one hand, to obtain energy consumption statistics 103, 103 'or to provide an energy consumption statistics 103, 103 '. The switch 201 may query the plurality of power consumption determining devices 100 ', 100 ", 100'" via multiple interrogation interfaces 107 ', 107 ", 107" to provide offers from different retailers via the interfaces 108', 108 ", 108"' receive. The switch 201 may in turn provide the client 203 with the cheapest offer or the cheapest energy consumption statistic prediction 104 '. However, a computing device 101 may detect when it is polled by a switch 201 and may refuse to provide a power consumption prediction 104. For detecting a switching device 201, the load curve can also be evaluated again and a fingerprint derived from the load curve can be used. In this case, a standard load curve 103 or a stationary load gear 103 may be detected and the computing device 101 may refuse to issue a power consumption forecast 104 or a quotation 104 if it receives such an unwanted energy consumption statistic.

In addition, the energy consumption determination device 100, in particular the computing device 101, may have a time measuring device which has an assumption of a provided offer 104 only within a certain predetermined time limit allows.

Fig. 3 FIG. 12 shows a load curve 103, an energy consumption statistics 103 or a load profile 103, according to an exemplary embodiment of the present invention. The time is plotted on the abscissa 301 and the consumption in MWh is plotted on the ordinate 305. The period of the illustrated energy consumption statistics 103 may include, for example, one year, two years or even three years. As an alternative to a diagram form, the load profile 103 may also be provided as an ordered list 103, where days are provided with times of the day evaluated with associated consumption values. Such an ordered list can be given for example in the form of an XML file, an XLS (X) file or a CSV file. With the help of the energy consumption statistics, load peaks 303, 302 and load valleys 304 can be determined, ie times of large quantities 302, 303 and small quantities 304. In a load peak 302, 303 a decrease peak takes place and in a load valley 304 a slight decrease takes place. An acceptance level may arise, for example, when the potential customer or the consumption statistics 103 have been recorded in a federal state which has a holiday or a bridging day at the assigned times. However, an acceptance level can also arise if at this time a wind turbine of the customer and / or a solar system becomes active. To be able to find out more precisely the cause of valleys or peaks, a comparison with external consumption parameters 105 can be made. In this case, the load curve 103 can be correlated with a large number of external consumption parameters and corresponding external consumption curves, in order for example to be able to find out and possibly compensate for the causes of decrease peaks or take-off levels. For example, external consumption curves may also be industry-specific consumption curves stored in the internal database. In another example, however, the evaluation of the load gear 103 can be used to find out whether there is a manipulation of a load profile. For example, if an acceptance level 304 is at a time when no holiday or other energy source is being used, it can be concluded that corresponding consumption values have been removed and / or moved from the statistics in this area. Thus, when a pick-up 304 occurs at a time when there is no holiday or when no other power source can be used, such as a solar system Darkness, it can be concluded that corresponding consumption values have been removed from the statistics in this area and / or postponed.

If an energy consumption prediction is to be generated, external consumption parameters 105, plausibility checks, and / or stored information from an internal database 109 may be used to analyze the consumption characteristic 103. The various information can be used to calculate, for example, a detected manipulation into the offer or into an energy consumption prediction by, for example, calculating a correspondingly higher value at a position which represents an acceptance level 304.

Fig. 4 FIG. 12 shows a flowchart for creating an artificial load profile 103, 103a, 103b according to an exemplary embodiment of the present invention. When creating an artificial load profile 103b, a menu 401 is offered by the computing device 101 to a customer or client via a portal. By means of different timelines 402, 403, 404, the daily energy consumption relative to one week can be indicated by means of controllers and / or slide switches 405, 406, 407. This method can be used if there is no load profile data of a recording load profile measurement (RLM). Or if no standard load profile (SLP) exists.

In the case of a registering load cycle measurement (RLM), a load profile of, for example, 35,040 measured values is provided in the form of an ordered list. To create the RLM, a measurement is performed on 365 days for 24 hours per quarter of an hour. By choosing the number of measurements, the resolution of an RLM can be increased or decreased.

An SLP is a statistical load profile. The creation of an SLP may be necessary if a totalizer is present, which can only measure the total power consumption over a predefinable time, and not, as for example in the case of an RLM, can perform a variety of individual measurements. An SLP may be shaped into an approximate RLM by taking into account, for example, characteristic power histories of the related industry, such as the RLM of a baker, a printing company or a chemical company. In this case industry-standard load profiles or synthesized load profiles can be used.

By means of the slide switches, a load profile 103a or a load gear 103a can thus be generated, which is not as accurate as an RLM, but is closer to the actual values of the load profile of the potential customer than would an SLP.

After an estimate of the daily consumption is indicated by means of the slide switches 405, 406, 407, the computing device 101 generates a new artificial load profile 103a. This artificial load profile 103a or standardized load profile 103a has a substantially rectangular course with normalized values. The values 405 ', 406' of the rectangular load path 103a substantially correspond to the switch values 405, 406. Instead of being a slide switch 405, 406, the input switches can also be realized as pie charts and / or controllers. In order to be able to make a prediction 104 or to be able to generate an energy consumption prediction 104, the standardized load profile 103, 103a, 103b is converted into a real-time load profile 103b with the aid of chronological curves or alternative load profiles. On the basis of the approximate real load profile, a good offer can be created, ie an offer that fits in well with the energy supplier's overall electricity portfolio and that takes good account of the consumption values of the customer. To create the approximately real load profile 103b, for example, an offset 407 can be provided which takes into account a basic requirement for energy in the load profile 103b. The offset may be calculated in addition or as an alternative to a constant, for example as a percentage of the total annual energy consumption and / or the daily energy consumption.

To create this approximately real load profile, values from the internal database and / or external consumption parameters 105 can be used. In other words, this may mean that, in order to arrive at a as realistic as possible load path 103b, the computing device 101 can take into account internally stored values. This may mean that the computing device 101 can interpolate a load profile from the storage of many provided load paths 103 of most different companies and / or industries. For example, it is possible to resort to load profile for specific industries. For example, you can use a profile for a manufacturing company, for a chemical company, for a printing company or for a manufacturing company. A potential customer can classify accordingly and an associated load profile can be used as empirical value in order to Combination with the standardized load curve 103a to arrive at a possible real load profile 103b. In the approximately real load profile 103b, the rectangular original energy indications 405 ', 406' are smoothed and interpolated to rounded consumption curves 405 ", 406" and shifted by an offset 407.

It is also possible for a potential customer 102 to provide a variety of different load cycles to obtain a common customer energy usage prediction 104. For example, a customer can request an offer for five different branches of his company. When calculating the total customer-specific combination energy consumption forecast from the individual energy consumption statistics of the branches, individual details of the individual companies, such as the postal codes of the locations, a wind statistic or a corresponding sunshine characteristic, are again included.

These individual energy consumption predictions may be combined internally to the total combined energy usage prediction and provided as a common customer energy consumption prediction 104. When creating the common customer-specific energy consumption forecast, the electricity supplier's existing and already purchased energy quantities 508 are also matched with the individual energy consumption statistics at specific times. The continuous check of the total energy prediction statistics 501, the total energy consumption prediction 501 or the total energy prediction curve 501 with each individual energy consumption statistic 103 can be achieved by a loop after each new intake of a new energy curve 103.

The existing and already purchased amounts of energy 508 may be represented in a total energy availability curve 508 or a total energy availability statistics 508. All energy consumption predictions 104 may be presented in the total energy consumption forecast 501. The total energy consumption prediction 501 may be represented as a total energy consumption prediction curve 501. The total energy consumption prediction curve 501 may be generated from the individual energy prediction curves 104 of the completed electricity supply contracts. It is also possible to regularly check whether there are new values for consumption parameters 105 that could lead to a new estimate of the total energy consumption forecast 501.

By comparing the total energy availability curve 508 and the total energy consumption prediction curve 501, it can be determined whether additional energy needs to be purchased for a certain period of time and / or whether it can be expected that this energy can be delivered to other companies. There is a regular review of this information and a regular review of the need, which exists and which pattern of energy consumption statistics 103, in particular by a new customer is preferable to fit well with the total energy availability curve 508 and / or the total energy consumption prediction curve 501. A customer with a particularly favorable load profile 103 can be moved, for example, by offering a favorable pricing by means of the computing device 101 to accept an offer in order to ensure the best possible energy utilization of the electricity supplier or the electricity trader. A quality parameter may be used to distinguish differently good energy consumption predictions 104. For example, the provision of an energy storage by a customer can be evaluated with a good quality parameter. In essence, it may be a goal of a power trader or a power generator to allow a load path 501 that is as constant as possible over as long a period as possible or a power supply 501 that is as constant as possible with a few peaks and valleys. Thus, for example, a customer whose energy consumption statistics 103 has an energy peak in the total energy prediction curve 501 or overall energy consumption prediction curve 501 of an energy supplier or electricity trader, or whose energy consumption statistic 103 matches the energy availability curve 508, may be attractive. Essentially, energy is only provided and can not be fed back, so excess energy would have to be resold during a power trough 304 or otherwise could not be used but must be paid for. In one example, however, can be done by using an energy storage backfeeding.

By providing a further interface 110, for example a trading interface 110 at the computing device 101, a trading system such as a power exchange can be accessed directly. At the moment of providing an offer 104 or accepting an offer 104, for example, electricity may be directly bought or sold on a stock exchange via such a trade interface 110, for example become. When determining a surplus of electricity or energy can be offered with the computing device 101 via the trade interface 110 and the energy surplus immediately after or before the contract on a power exchange for resale.

An external consumption parameter 105 may be an HPFC (Hourly Price Forward Curve). Such an HPFC may be a purchase value traded on a power exchange and pricing an amount of energy. Such an HPFC is essentially based on averages and a smooth course of the load. It can be offered on a power exchange but also substantially rectangular energy packets that provide a constant amount of energy at certain times or months. Such an external parameter or a combination of rectangular energy availability values can also be reconverted into a smoothed history by comparison with empirical values and / or stored statistics in order to determine which amount of energy is available for a given day and to resell an energy surplus and / or to be able to store in an energy store.

For a quantity of energy that is not covered by procurement, there may be a so-called spot price risk. This may mean that if the total energy prediction 501 and / or the total energy consumption prediction curve 501, and in particular the total energy supply curve 508, is incorrect, then the required amount of energy must be purchased at a daily price, which is typically higher than a price of the energy reached in advance planning can. A criterion of whether a provided load profile 103 fits well with the portfolio or a total energy forecasting curve 501 of the electricity trader or the energy supplier may be to avoid a spot price. In other words, an electricity supplier may try to make an overall energy prediction curve, where in the total energy prediction curve or total energy consumption prediction curve 501 all load paths of completed supply contracts or all energy forecasts 104 may be superimposed. As far as possible, the electricity supplier or electricity distributor may cover this entire load profile curve 501 with various products which are provided on a power exchange or by a utility. The provided power consumption values or energy consumption values may essentially be rectangular processes. For example, a base product may include a base cover ensure constant values throughout the year. To this basic supply, peak products can be added, which provide certain constant amounts of energy for individual periods. From the various packages of electricity supply, it may be possible to create a substantially rectangular overall supply curve or an approximately rectangular shopping area. Such a shopping load 508, for example, the curve 508 from Fig. 5 represent.

The Fig. 5 FIG. 5 shows a combination diagram 500 of a total energy consumption forecasting curve 501 and a partial total energy supply curve 508 or purchasing curve 508 according to an exemplary embodiment of the present invention. The total load curve 501, the current portfolio 501, or the electricity mix 501 of the electricity trader indicates when a particular amount of electricity and / or amount of energy is required according to the total energy prediction. This total load curve 501 is covered by various energy exchanges standard products, which together form the total energy supply curve 508. For example, there is a constant annual supply 502 and peak products 505, 506, 507, which are combined to cover the total load curve 501 as well as possible. In order to cover the actual expected total load 501, it may be provided to pay spot products to intercept a shortage resulting, for example, in area 504 ', for example, between the total load curve 501 and the shopping aisle 508. However, there are also areas in where there is an oversupply, for example in the areas 503 ', 503 ", 503"'. In these areas, energy can be resold on electricity markets or stored in energy storage between. For example, if the shopping load 508 is above the total load curve 501.

In one example, after a potential new customer has agreed to an offer, the overall energy usage prediction curve 501 may be adjusted accordingly with the newly added additional load curve 103, and in particular the energy consumption prediction 104 derived therefrom. According to the new total load 501 thus formed, the newly required energy demand from a purchasing product 502, 503, 505, 506, 507 can be reproduced and so over an automatic access 110 to a trading platform superfluous energy offered as well as needed energy can be bought new or an instruction to a control program for an energy store to be created. This purchase and sale of energy can essentially be performed automatically by the computing device 101.

Fig. 6 FIG. 12 is a flowchart for a method of determining power consumption according to an exemplary embodiment of the present invention. FIG. The method of determining power consumption may begin in a startup state S601. In a state S602, the method may provide for receiving an energy consumption statistic and for receiving at least one external consumption parameter. In the state S603, the determination of an energy consumption prediction is made from the received energy consumption statistics and the at least one external consumption parameter. In state S604, the power consumption prediction and / or offer is provided. In the end state S605 the process ends.

In addition, it should be noted that "comprising" and "having" does not exclude other elements or steps, and "a" or "an" does not exclude a multitude. It should also be appreciated that features or steps described with reference to any of the above embodiments may also be used in combination with other features or steps of other embodiments described above. Reference signs in the claims are not to be considered as limiting.

Claims (9)

Energy consumption determination apparatus (100), comprising: an energy input interface (107) for receiving an energy consumption statistic (103); a parameter input interface (106) for receiving at least one external consumption parameter (105); a computing device (101); an output interface (108) for providing a power usage prediction (104); wherein the computing device (101) is arranged to identify an end user (102) by evaluating the energy consumption statistics (103) and / or to detect a manipulated energy consumption statistics (103); wherein the computing device (101) is arranged to determine the energy consumption prediction (104) from the energy consumption statistics (103) and the at least one external consumption parameter (105). The energy consumption determination apparatus (100) of claim 1, wherein the at least one consumption parameter (105) is at least one parameter selected from the group of parameters consisting of: Energy costs; Sunshine statistics; Wind statistics; and Calendar. The energy consumption determination apparatus (100) of claim 1 or 2, wherein the power input interface (107) and / or the output interface (108) is / are adapted to interface with / enable communication with a client (102). Energy consumption determination apparatus (100) according to any one of claims 1 to 3, wherein the power input interface (107, 401) for creating a Energy consumption statistics (103 a, 103 b) is set up. The energy consumption determination apparatus (100) of any one of claims 1 to 4, further comprising: a trade interface (110); wherein the trading interface (110) is adapted to purchase and / or sell energy. The energy consumption determination apparatus (100) according to any one of claims 1 to 5, wherein the calculating means (101) is arranged to design a presentation menu (401) in accordance with the energy consumption statistics (103). Energy consumption determination apparatus (100) according to one of claims 1 to 6, further comprising: a time measuring device; wherein the time measuring means is arranged to delete the validity of a power consumption prediction (104) after a predeterminable time. System (200) for determining an energy consumption, comprising: a switch (201); a plurality of energy consumption determination devices (100 ', 100 ", 100'") according to any one of claims 1 to 6; wherein the switch (201) is connected to the plurality of power consumption determining devices (100 ', 100 ", 100"'); and wherein the switch (201) is arranged to poll a plurality of energy consumption predictions (104) from the plurality of energy consumption determination devices (100 ', 100 ", 100"') and generate a single energy consumption prediction (104 ') therefrom. A method of determining power consumption, comprising: Receiving an energy consumption statistic; Receiving at least one external consumption parameter; Evaluation of energy consumption statistics; Identify an end user and / or detect a compromised one Energy consumption statistics with the evaluated energy consumption statistics; Determining an energy consumption prediction from the energy consumption statistics and the at least one external consumption parameter, Provide the energy consumption forecast.

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