CN117495056A - Power consumption data monitoring and optimizing method and system - Google Patents
Power consumption data monitoring and optimizing method and system Download PDFInfo
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Abstract
The application relates to the technical field of electricity consumption monitoring, in particular to an electricity consumption data monitoring and optimizing method and system. According to the method and the device, the electricity consumption data of the user to be monitored, the current electricity consumption charging mode and the electricity charge unit price parameter are acquired, the electricity consumption cost of the user is calculated, then whether the electricity consumption cost of the user is larger than the historical electricity consumption cost is judged, and when the electricity consumption cost of the user is larger than the historical electricity consumption cost, the electricity consumption adjustment advice for the user is determined and output according to the electricity consumption data, so that the user can timely adjust the electricity consumption scheme, the electricity consumption cost of the user is saved, and energy is saved.
Description
Technical Field
The application relates to the technical field of electricity consumption monitoring, in particular to an electricity consumption data monitoring and optimizing method and system.
Background
At present, the definition of the energy Internet is to take a power system as a center, take a smart grid as a backbone, take Internet, big data, cloud computing and other leading information communication technologies as ties, comprehensively apply advanced power electronic technology and intelligent management technology, and can realize a next generation energy system of high fusion of energy and information with transverse multi-energy complementation and longitudinal source-network-charge-storage coordination. The user is used as a 'load' end in the energy Internet, is an important component part and a service object of the energy Internet, and the energy efficiency management at the user side is one of key technologies of the energy Internet.
The existing equipment such as intelligent ammeter can collect electric energy use data, but users need to record electricity data by themselves, and the electricity consumption scheme cannot be adjusted according to the electricity consumption data in time, so that the electricity consumption cost of the users is increased.
Disclosure of Invention
In order to timely adjust the power consumption scheme according to the power consumption data, the application provides a power consumption data monitoring and optimizing method and system, which adopts the following technical scheme:
in a first aspect, the present application provides a method for monitoring and optimizing electricity consumption data, comprising the steps of:
acquiring electricity consumption data of a user to be monitored, a current electricity consumption charging mode and a current electricity charge unit price parameter, wherein the electricity consumption data is in a first preset time period;
calculating the electricity consumption cost of a user according to the electricity consumption data, the current electricity consumption charging mode and the current electricity charge unit price parameter;
judging whether the electricity consumption cost of the user is larger than the historical electricity consumption cost, wherein the historical electricity consumption cost is the electricity consumption cost of the user to be monitored in a second preset time period, and the second preset time period is earlier than the first preset time period;
and when the electricity consumption cost of the user is greater than the historical electricity consumption cost, determining an electricity consumption adjustment suggestion for the user according to the electricity consumption data and sending the suggestion to a user terminal.
Through adopting above-mentioned technical scheme, this application is through obtaining user's power consumption data, current power consumption billing mode and the unit price parameter of charges of electricity of waiting to monitor, calculates user's power consumption cost, then judges whether user's power consumption cost is greater than historical power consumption cost, when being greater than historical power consumption cost, confirms and exports the power consumption regulation suggestion to the user according to power consumption data to make the user in time adjust the power consumption scheme, practice thrift user's power consumption cost, the energy saving.
Optionally, the current electricity charging mode is any one of single electricity price charging, time-of-use electricity price charging, step electricity price charging, required electricity price charging and capacity electricity price charging.
By adopting the technical scheme, the electricity consumption charging modes comprise a plurality of single electricity price charging, time-of-use electricity price charging, step electricity price charging, required electricity price charging and capacity electricity price charging, the cost calculation modes under different electricity consumption charging modes are different, the electricity consumption cost of a user can be calculated more accurately by combining different electricity consumption charging modes, and the electricity consumption scheme can be adjusted better.
Optionally, when the electricity consumption cost of the user is greater than the historical electricity consumption cost, determining an electricity consumption adjustment suggestion for the user according to the electricity consumption data, and sending the suggestion to a user terminal, wherein the method specifically comprises the following steps:
acquiring a cost difference value between the user electricity cost and the historical electricity cost;
determining user reducible electricity consumption data according to the electricity consumption data, the cost difference value, the current electricity consumption charging mode and the current electricity fee unit price parameter, wherein the reducible electricity consumption data comprises one or more of total electricity consumption, electricity consumption in peak time and maximum demand;
and determining power consumption adjustment suggestions for the user according to the reducible power consumption data and sending the power consumption adjustment suggestions to a user terminal.
Through adopting above-mentioned technical scheme, this application is through obtaining the cost difference between user's electricity consumption cost and the historical electricity consumption cost, according to electricity consumption data, cost difference, current electricity consumption billing mode and electric charge unit price parameter, confirm that the user can reduce the electricity consumption data, including total amount of electricity consumption, peak time electricity consumption, maximum demand etc. to instruct the user to adjust the electricity consumption better.
Optionally, the determining, according to the reducible electricity consumption data, an electricity consumption adjustment suggestion for the user and sending the suggestion to a user terminal specifically includes:
calculating an acceptance coefficient alpha of the user on the user adjustment suggestion according to the history n times of user adjustment suggestions;
calculating target electricity consumption data according to the reducible electricity consumption data and the acceptance coefficient;
and determining power consumption adjustment suggestions for the user according to the target power consumption data and sending the power consumption adjustment suggestions to a user terminal.
By adopting the technical scheme, the method and the device calculate the acceptance coefficient alpha of the user for the adjustment advice according to the historical n-time user adjustment advice, then calculate the target electricity data by combining the acceptance coefficient and the reducible electricity data, determine and output the electricity adjustment advice for the user according to the target electricity data, and therefore the electricity adjustment advice is more in line with the acceptance of the user.
Optionally, after determining the power consumption adjustment suggestion for the user according to the power consumption data and sending the power consumption adjustment suggestion to the user terminal, the method further includes:
acquiring an electricity charging mode and an electricity price unit price parameter of a user location;
establishing an electricity cost formula corresponding to different electricity charging modes according to the electricity charging modes and the electricity price monovalent parameters;
inputting the electricity consumption data into an electricity consumption cost formula corresponding to the different electricity consumption charging modes, and obtaining electricity consumption cost under the different electricity consumption charging modes;
determining an optimal electricity charging mode according to the electricity cost under different electricity charging modes;
and judging whether the current electricity charging mode is the same as the optimal electricity charging mode, and if so, outputting an electricity charging mode adjustment suggestion.
Through adopting above-mentioned technical scheme, this application establishes the electricity cost formula that different electricity charging modes correspond through obtaining the electricity charging mode and the monovalent parameter of charges of electricity at user place, then obtains the electricity cost of user under different electricity charging modes, confirms best electricity charging mode, then judges whether current electricity charging mode is the same with best electricity charging mode, and output electricity charging mode regulation suggestion when not simultaneously to make the user can select more suitable electricity charging mode, reduce the electricity cost.
Optionally, the obtaining the electricity charging mode and the electricity price unit price parameter of the user location specifically includes:
obtaining geographic position information of a user location;
acquiring an electricity price charging policy corresponding to the geographic position according to the geographic position information;
and extracting a power consumption charging mode and a power cost unit price parameter according to the power cost charging policy.
By adopting the technical scheme, different geographic area policies have different electricity charging modes and electricity charge unit price parameters, and the application obtains the corresponding electricity charge policy by obtaining the geographic position information of the user location, and then extracts the electricity charging modes and the electricity charge unit price parameters, thereby automatically identifying the available electricity charging modes of the user location.
Optionally, the acquiring the electricity consumption data of the user to be monitored specifically includes:
determining correlation factors affecting electricity data, the correlation factors including time, weather, electricity preference and holidays;
training a preset prediction model according to historical electricity consumption data and historical correlation factors to obtain an electricity consumption data prediction model, wherein the electricity consumption data prediction model is y [ i ] =beta [ i0] +beta [ i1] x1+beta [ i2] x2+ + beta [ in ] xn+epsilon, wherein y [ i ] represents the electricity consumption data, x1 and x 2..xn represent normalized data corresponding to the correlation factors, beta [ i0]. Beta [ in ] is a model parameter corresponding to the electricity consumption data, and epsilon is an error;
and acquiring a target correlation factor of a first preset time period, and inputting the target correlation factor into the electricity consumption data prediction model to obtain electricity consumption data.
Through adopting the technical scheme, the power consumption data prediction model is obtained by determining the correlation factor influencing the power consumption data and training the preset prediction model according to the historical power consumption data and the historical correlation factor, then the target correlation factor in the first preset time period is acquired, the target correlation factor is input into the power consumption data prediction model, the prediction of the power consumption data is realized, and the power consumption regulation suggestion is output in advance, so that the power consumption cost of a user is saved.
In a second aspect, the present application provides a power usage data monitoring and optimization system comprising:
the parameter acquisition module is used for acquiring electricity consumption data of a user to be monitored, a current electricity consumption charging mode and a current electricity fee unit price parameter, wherein the electricity consumption data is in a first preset time period;
the cost calculation module is used for calculating the electricity consumption cost of the user according to the electricity consumption data, the current electricity consumption charging mode and the current electricity charge unit price parameter;
the judging module is used for judging whether the electricity consumption cost of the user is larger than the historical electricity consumption cost, wherein the historical electricity consumption cost is the electricity consumption cost of the user to be monitored in a second preset time period, and the second preset time period is earlier than the first preset time period;
and the adjustment advice output module is used for determining an electricity consumption adjustment advice for the user according to the electricity consumption data and sending the electricity consumption adjustment advice to a user terminal when the electricity consumption cost of the user is greater than the historical electricity consumption cost.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above-described power usage data monitoring and optimization method when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above-described electricity usage data monitoring and optimization method.
In summary, the present application at least includes the following beneficial technical effects:
according to the method and the device, the electricity consumption cost of the user is calculated by acquiring the electricity consumption data of the user to be monitored, the current electricity consumption charging mode and the electricity charge unit price parameter, then whether the electricity consumption cost of the user is larger than the historical electricity consumption cost is judged, and when the electricity consumption cost of the user is larger than the historical electricity consumption cost, the electricity consumption adjustment advice of the user is determined and output according to the electricity consumption data, so that the user can timely adjust the electricity consumption scheme, the electricity consumption cost of the user is saved, and energy is saved;
according to the method, according to the historical n-time user adjustment suggestions, an acceptance coefficient alpha of the user on the adjustment suggestions is calculated, then target electricity utilization data is calculated by combining the acceptance coefficient and the reducible electricity utilization data, and the electricity utilization adjustment suggestions for the user are determined and output according to the target electricity utilization data, so that the electricity utilization adjustment suggestions more accord with the acceptance degree of the user;
according to the method and the device, the correlation factors influencing the electricity consumption data are determined, the preset prediction model is trained according to the historical electricity consumption data and the historical correlation factors, the electricity consumption data prediction model is obtained, then the target correlation factors in the first preset time period are obtained, the target correlation factors are input into the electricity consumption data prediction model, the prediction of the electricity consumption data is achieved, and therefore electricity consumption adjustment suggestions are output in advance, and electricity consumption cost of a user is saved.
Drawings
FIG. 1 is an exemplary flow chart of a method of power usage data monitoring and optimization in accordance with an embodiment of the present application;
FIG. 2 is an exemplary flow chart for predicting electricity usage data in accordance with an embodiment of the present application;
FIG. 3 is an exemplary flow chart for determining power usage adjustment suggestions to a user according to an embodiment of the present application;
FIG. 4 is an exemplary flow chart for determining power usage adjustment recommendations based on target power usage data according to an embodiment of the present application;
FIG. 5 is an exemplary flow chart of outputting an electric billing mode adjustment recommendation according to an embodiment of the present application;
FIG. 6 is a block diagram of a power usage data monitoring and optimization system according to an embodiment of the present application;
fig. 7 is an internal structural diagram of an electronic device according to an embodiment of the present application.
Description of the embodiments
The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this application is intended to encompass any or all possible combinations of one or more of the listed items.
The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
The existing equipment such as intelligent ammeter can collect electric energy use data, but users need to record electricity data by themselves, and the electricity consumption scheme cannot be adjusted according to the electricity consumption data in time, so that the electricity consumption cost of the users is increased.
The utility model provides a power consumption data monitoring and optimizing method and system, through obtaining the power consumption data of waiting to monitor user, current power consumption charging mode and electric charge unit price parameter, calculate user's power consumption cost, then judge whether user's power consumption cost is greater than historical power consumption cost, when being greater than historical power consumption cost, confirm and export the power consumption regulation suggestion to the user according to power consumption data to make the user in time adjust the power consumption scheme, practice thrift user's power consumption cost, the energy saving.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The method is executed by an electronic device, and the electronic device can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server for providing cloud computing service. In this embodiment, the terminal device is an electronic device, but not limited to this, but may also be an intelligent tablet, a computer, or the like, where the terminal device and the server may be directly or indirectly connected through a wired or wireless communication manner, and the embodiment of the present application is not limited herein.
Referring to fig. 1, fig. 1 is an exemplary flowchart of a method for power usage data monitoring and optimization in accordance with an embodiment of the present application.
In a first aspect, the present application provides a method for power usage data monitoring and optimization, comprising the steps of;
s110, acquiring electricity consumption data of a user to be monitored, a current electricity consumption charging mode and a current electricity fee unit price parameter, wherein the electricity consumption data is the electricity consumption data in a first preset time period.
The current electricity consumption charging mode is any one of single electricity price charging, time-sharing electricity price charging, step electricity price charging, required electricity price charging and capacity electricity price charging. The electricity consumption data comprise the total amount of electricity consumption, the maximum required amount, the electricity consumption in the peak period, the electricity consumption in the valley period, the electricity consumption in the usual period and the like, and the electricity consumption data can be directly obtained through the intelligent ammeter. Specifically, the first preset time period may be one month.
In one embodiment, referring to FIG. 2, the model may also be used to predict electricity usage data. In particular, the method comprises the steps of,
s111, determining a correlation factor influencing the power consumption data.
Among other relevant factors that affect the electricity usage data include, but are not limited to, time, weather, electricity usage preferences, holidays, etc., for example, the longer the total electricity usage time, the more total electricity usage, assuming that the longer the number of days of the month. The longer the time of the electric equipment such as an air conditioner which needs to be used is, the more total electricity consumption is assumed to be, the larger the electricity consumption in the peak time is, and the larger the maximum demand is. It is assumed that the more holidays in the month, the longer the user's home period, the more electricity consumption, and so on.
And S112, training a preset prediction model according to the historical electricity consumption data and the historical correlation factors to obtain an electricity consumption data prediction model.
The electricity consumption data prediction model is y [ i ] =beta [ i0] +beta [ i1] x 1+beta [ i2] x2+ & ltbeta [ in ] xn+epsilon, y [ i ] represents electricity consumption data, i can be total electricity consumption, maximum required amount, peak electricity consumption, valley electricity consumption, normal time electricity consumption and the like, x1 and x 2..xn represent normalized data corresponding to a correlation factor, beta [ i0]. Beta [ in ] is a model parameter corresponding to the corresponding electricity consumption data, and epsilon is an error.
And determining the influence degree of each correlation factor on each power consumption data through statistical data, wherein for example, weather has a larger influence on the total power consumption amount, the maximum demand amount and the power consumption amount in the peak time, and setting model parameters corresponding to the weather according to the influence. Holidays have influence on the total power consumption and the maximum demand, and have influence on the power consumption in the normal time, and then model parameters corresponding to the holidays are set.
It will be appreciated that different correlation factors have different degrees of influence on different electricity usage data. Before calculation, normalization processing is needed to be performed on the correlation factors, for example, the user electricity preference is scored, and the correlation factors are mapped to a range of 0-1 according to the score, holidays can be represented by binary variables, workdays are set to 0, and holidays are set to 1.
Specifically, the historical electricity consumption data sets are collected, various electricity consumption data including total electricity consumption, maximum demand, peak electricity consumption and the like, and the relevant factor data sets affecting the electricity consumption data are collected, wherein the relevant factor data sets include weather data, holiday data, electricity preference data and the like. And then extracting data of a relevant factor, such as weather data, from each user parameter data set, repeatedly collecting historical electricity data sets, and analyzing the influence degree of the weather data on each electricity data under the condition that other relevant factors are similar. And then continuously extracting data of other related factors to obtain the influence degree of all the related factors on the power consumption data, comprehensively sequencing the influence degree of all the related factors on all the power consumption data, and selecting the first N related factors with the largest influence degree. And determining the weight of each correlation factor in the prediction model, namely the value of the parameter beta, and applying the influence factors and the weights thereof to the prediction model to obtain a trained model.
S113, acquiring a target correlation factor of a first preset time period, and inputting the target correlation factor into the electricity consumption data prediction model to obtain electricity consumption data.
Specifically, according to information such as weather forecast, calendar, daily activities of users and the like, target correlation factors in a first preset time period can be predicted, so that electricity consumption data can be obtained according to an electricity consumption data prediction model.
It can be understood that the cost calculation modes in different electricity charging modes are different, and the electricity data to be used are different, and the electricity data acquired in step S110 includes all the electricity data to be adopted in each electricity charging mode. The user electricity cost can be calculated more accurately by combining different electricity charging modes, and the electricity consumption scheme can be adjusted better.
The weather condition of the first preset time period can be determined according to weather forecast, holiday information and time of month of the first preset time period can be determined according to a calendar, electricity consumption preference of a user in specific weather condition and on holidays can be determined according to daily activities of the user, and then target relevant factors of the user in the first preset time period can be estimated.
And S120, calculating the electricity consumption cost of the user according to the electricity consumption data, the current electricity consumption charging mode and the current electricity charge unit price parameter.
The user electricity cost is calculated according to the electricity data, the current electricity charging mode and the electricity price unit price parameter. For example, when the current electricity charging mode is a single electricity price charging mode, the calculation formula of the user cost is that cost=electricity consumption is equal to the single electricity price. For the time-sharing electricity price charging mode, the user cost calculation formula is cost=electricity price at peak time of electricity consumption peak time period, electricity consumption level time period, electricity price at ordinary time and electricity consumption valley time period. For the step electricity price charging mode, the user cost calculation formula is that cost=electricity consumption step 1, step 1 electricity price+electricity consumption step 2, step 2 electricity price+ &. For the electricity price charging mode, the user cost calculation formula is cost=basic electricity charge+maximum electricity price.
And S130, judging whether the electricity consumption cost of the user is larger than the historical electricity consumption cost, wherein the historical electricity consumption cost is the electricity consumption cost of the user to be monitored in a second preset time period, and the second preset time period is earlier than the first preset time period.
The second preset time period may be a time period of the same period as the first preset time period, for example, when the first preset time period is a month, the second preset time period is a month of the last year.
It will be appreciated that when the user electricity costs are greater than the historical electricity costs, the user electricity costs increase and electricity adjustments are required.
And S140, when the electricity consumption cost of the user is greater than the historical electricity consumption cost, determining an electricity consumption adjustment suggestion for the user according to the electricity consumption data, and sending the suggestion to the user terminal.
Specifically, referring to fig. 3, step S140 includes;
s141, acquiring a cost difference value between the user electricity consumption cost and the historical electricity consumption cost.
And comparing the calculated user electricity cost with the historical electricity cost to obtain a cost difference value.
S142, determining the user-reducible electricity consumption data according to the electricity consumption data, the cost difference value, the current electricity consumption charging mode and the current electricity charge unit price parameter.
Wherein the reducible electricity consumption data includes one or more of total electricity consumption, peak electricity consumption, and maximum demand. And determining the user reducible power consumption data according to the cost difference value, the current power consumption charging mode and the power charge unit price parameter, for example, in the power consumption charging mode of time-sharing power price charging, if the peak power price is higher and the power consumption is higher, determining the power consumption in the peak period as reducible power consumption data, and determining the power consumption in the peak period to be reduced according to the peak power price and the cost difference value.
S143, determining and outputting power consumption adjustment suggestions for the user according to the reducible power consumption data.
Specifically, the power consumption adjustment advice for the user is output, and the user is advised to reduce the determined reducible power consumption data, so that the user can perform power consumption adjustment according to the power consumption adjustment advice, and the effect of reducing the power consumption cost is achieved.
In one embodiment, referring to fig. 4, the method further comprises:
s144, calculating an acceptance coefficient alpha of the user on the adjustment suggestion according to the history n times of user adjustment suggestions.
Wherein the acceptance coefficient α= Σ (power consumption data recommended adjustment amount-power consumption data actual adjustment amount)/n.
It can be appreciated that after the user adjustment advice is outputted a plurality of times, in order to determine the adjustment advice more in line with the actual situation of the user later, the acceptance coefficient of the user to the adjustment advice is calculated according to the historical N times of adjustment advice.
S145, calculating target electricity consumption data according to the electricity consumption data and the acceptance coefficient.
Specifically, the power consumption data and the adjustment amount to be adjusted are determined according to the power consumption data, and then final target power consumption data is determined according to the acceptance coefficient, so that a user is assisted to slowly improve the power consumption behavior through long-term power consumption adjustment.
S146, according to the target electricity consumption data, the electricity consumption adjustment suggestion of the user is sent to the user terminal.
In one embodiment, referring to fig. 5, step S140 further includes:
s150, acquiring an electricity charging mode and an electricity price unit price parameter of the user location.
The electricity charging modes and the electricity price parameters of different areas are different, and a plurality of electricity charging modes are generally selected by users, so that the demands of the users are met. On the user side, selecting different electricity charging modes can have a great influence on electricity cost.
For example, if the electricity consumption of the customer is small in the peak period and large in the valley period, the electricity charge can be greatly reduced by selecting the time-of-use electricity price charging mode.
Specifically, step S150 includes:
s151, obtaining geographic position information of the user location.
And acquiring the geographical position information of the user by using the ammeter information bound by the user.
S152, acquiring the electricity price charging policy corresponding to the geographic position according to the geographic position information.
According to the city of the user, searching a power grid company in the region, and matching corresponding electricity price charging policy files in public files of the power grid company.
S153, extracting the electricity consumption charging mode and the electricity price unit price parameter according to the electricity price charging policy.
Specifically, the regular expression is used for matching with the electricity price description text in the policy file, the electricity price description text is analyzed, the charging mode keywords such as time-sharing electricity price and step electricity price are identified, then the electricity price unit price value in the description text is further extracted as a parameter, and the electricity charging mode and the electricity price unit price parameter are stored in a database.
S160, establishing a power consumption cost formula corresponding to different power consumption charging modes according to the power consumption charging modes and the unit price parameters of the electric charge.
Wherein the electricity cost formula has been given at step S120.
S170, inputting the electricity consumption data into an electricity consumption cost formula corresponding to different electricity consumption charging modes, and obtaining the electricity consumption cost under the different electricity consumption charging modes.
When the electricity consumption data is obtained, corresponding calculation parameters are obtained according to the electricity consumption cost formulas corresponding to different electricity consumption charging modes, and then are substituted into the formulas to calculate, so that the electricity consumption cost under different electricity consumption charging modes can be obtained.
S180, determining an optimal electricity charging mode according to the electricity cost in different electricity charging modes.
By comparing the electricity cost calculated under different electricity charging modes, the electricity charging mode with the lowest electricity cost can be determined to be the optimal electricity charging mode.
S190, judging whether the current electricity charging mode is the same as the optimal electricity charging mode, and if so, outputting an electricity charging mode adjustment suggestion.
Specifically, for example, the current electricity charging mode of the user is charging according to a single electricity price, and because the electricity charging time period of the user is a normal time period and a low-peak time period, the electricity consumption in the electricity consumption peak time period is less, the cost of the mode of time-sharing electricity price charging is found to be lower through calculation, at the moment, an electricity charging mode adjustment suggestion is output, and the user is suggested to adjust to a cadaver electricity price charging mode, so that the electricity consumption cost of the user is saved.
In a second aspect, the present application provides an electricity data monitoring and optimizing system, and the electricity data monitoring and optimizing system of the present application is described below in conjunction with the above-mentioned electricity data monitoring and optimizing method. Referring to fig. 6, fig. 6 is a schematic block diagram of a power consumption data monitoring and optimizing system according to an embodiment of the present application.
A power usage data monitoring and optimization system comprising:
the parameter obtaining module 510 is configured to obtain electricity consumption data of a user to be monitored, a current electricity consumption charging mode, and a current electricity fee unit price parameter, where the electricity consumption data is electricity consumption data in a first preset time period;
the cost calculation module 520 is configured to calculate a user electricity cost according to the electricity data, the current electricity charging mode, and the current electricity rate unit price parameter;
a judging module 530, configured to judge whether the user electricity consumption cost is greater than a historical electricity consumption cost, where the historical electricity consumption cost is the electricity consumption cost of the user to be monitored in a second preset time period, and the second preset time period is earlier than the first preset time period;
and the adjustment advice output module 540 is configured to determine an adjustment advice for electricity consumption of the user according to the electricity consumption data and send the adjustment advice to the user terminal when the electricity consumption cost of the user is greater than the historical electricity consumption cost.
In one embodiment, the present application provides an electronic device, which may be a server, whose internal structure may be as shown in fig. 7. The electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the electronic device is for storing data. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of power usage data monitoring and optimization.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the electronic device to which the present application is applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided an electronic device including a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method embodiments described above when executing the computer program.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The foregoing are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in any way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (8)
1. A method for monitoring and optimizing electricity consumption data, comprising the steps of:
acquiring electricity consumption data of a user to be monitored, a current electricity consumption charging mode and a current electricity charge unit price parameter, wherein the electricity consumption data is in a first preset time period;
calculating the electricity consumption cost of a user according to the electricity consumption data, the current electricity consumption charging mode and the current electricity charge unit price parameter;
judging whether the electricity consumption cost of the user is larger than the historical electricity consumption cost, wherein the historical electricity consumption cost is the electricity consumption cost of the user to be monitored in a second preset time period, and the second preset time period is earlier than the first preset time period;
and when the electricity consumption cost of the user is greater than the historical electricity consumption cost, determining an electricity consumption adjustment suggestion for the user according to the electricity consumption data and sending the suggestion to a user terminal.
2. The power consumption data monitoring and optimizing method according to claim 1, wherein the current power consumption charging mode is any one of single power price charging, time-of-use power price charging, step-by-step power price charging, demand power price charging and capacity power price charging.
3. The electricity consumption data monitoring and optimizing method according to claim 1, wherein when the electricity consumption cost of the user is greater than the historical electricity consumption cost, determining an electricity consumption adjustment suggestion for the user according to the electricity consumption data and sending the suggestion to a user terminal, specifically comprising:
acquiring a cost difference value between the user electricity cost and the historical electricity cost;
determining user reducible electricity consumption data according to the electricity consumption data, the cost difference value, the current electricity consumption charging mode and the current electricity fee unit price parameter, wherein the reducible electricity consumption data comprises one or more of total electricity consumption, electricity consumption in peak time and maximum demand;
and determining power consumption adjustment suggestions for the user according to the reducible power consumption data and sending the power consumption adjustment suggestions to a user terminal.
4. A power usage data monitoring and optimization method according to claim 3, wherein said determining a power usage adjustment recommendation to said user based on said reducible power usage data and transmitting to a user terminal, in particular comprising:
calculating an acceptance coefficient alpha of the user on the user adjustment suggestion according to the history n times of user adjustment suggestions;
calculating target electricity consumption data according to the reducible electricity consumption data and the acceptance coefficient;
and determining power consumption adjustment suggestions for the user according to the target power consumption data and sending the power consumption adjustment suggestions to a user terminal.
5. The electricity usage data monitoring and optimization method of claim 1, wherein after determining an electricity usage adjustment recommendation for the user based on the electricity usage data and transmitting the recommendation to a user terminal, further comprising:
acquiring an electricity charging mode and an electricity price unit price parameter of a user location;
establishing an electricity cost formula corresponding to different electricity charging modes according to the electricity charging modes and the electricity price monovalent parameters;
inputting the electricity consumption data into an electricity consumption cost formula corresponding to the different electricity consumption charging modes, and obtaining electricity consumption cost under the different electricity consumption charging modes;
determining an optimal electricity charging mode according to the electricity cost under different electricity charging modes;
and judging whether the current electricity charging mode is the same as the optimal electricity charging mode, and if so, outputting an electricity charging mode adjustment suggestion.
6. The method for monitoring and optimizing electricity consumption data according to claim 5, wherein the step of obtaining the electricity consumption charging mode and the electricity fee unit price parameter of the user location specifically comprises the following steps:
obtaining geographic position information of a user location;
acquiring an electricity price charging policy corresponding to the geographic position according to the geographic position information;
and extracting a power consumption charging mode and a power cost unit price parameter according to the power cost charging policy.
7. The electricity consumption data monitoring and optimizing method according to claim 1, wherein the obtaining electricity consumption data of the user to be monitored specifically includes:
determining correlation factors affecting electricity data, the correlation factors including time, weather, electricity preference and holidays;
training a preset prediction model according to historical electricity consumption data and historical correlation factors to obtain an electricity consumption data prediction model, wherein the electricity consumption data prediction model is y [ i ] =beta [ i0] +beta [ i1] x1+beta [ i2] x2+ + beta [ in ] xn+epsilon, wherein y [ i ] represents the electricity consumption data, x1 and x 2..xn represent normalized data corresponding to the correlation factors, beta [ i0]. Beta [ in ] is a model parameter corresponding to the electricity consumption data, and epsilon is an error;
and acquiring a target correlation factor of a first preset time period, and inputting the target correlation factor into the electricity consumption data prediction model to obtain electricity consumption data.
8. A power usage data monitoring and optimization system, comprising:
the parameter acquisition module is used for acquiring electricity consumption data of a user to be monitored, a current electricity consumption charging mode and a current electricity fee unit price parameter, wherein the electricity consumption data is in a first preset time period;
the cost calculation module is used for calculating the electricity consumption cost of the user according to the electricity consumption data, the current electricity consumption charging mode and the current electricity charge unit price parameter;
the judging module is used for judging whether the electricity consumption cost of the user is larger than the historical electricity consumption cost, wherein the historical electricity consumption cost is the electricity consumption cost of the user to be monitored in a second preset time period, and the second preset time period is earlier than the first preset time period;
and the adjustment advice output module is used for determining an electricity consumption adjustment advice for the user according to the electricity consumption data and sending the electricity consumption adjustment advice to a user terminal when the electricity consumption cost of the user is greater than the historical electricity consumption cost.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107203826A (en) * | 2017-05-19 | 2017-09-26 | 浙江大学 | A kind of power program optimization method of industrial user |
| CN110298509A (en) * | 2019-06-28 | 2019-10-01 | 佰聆数据股份有限公司 | A kind of big industrial trade Electric optimization of combination short-term load forecasting |
| CN111445205A (en) * | 2020-03-25 | 2020-07-24 | 天纳能源科技（上海）有限公司 | Electrical load control system |
| CN113095595A (en) * | 2021-05-06 | 2021-07-09 | 广东鹰视能效科技有限公司 | Energy efficiency optimization method and system based on power distribution operation and maintenance |
| CN115730790A (en) * | 2022-11-14 | 2023-03-03 | 四川新仑科技有限公司 | Charging configuration method, device and equipment based on edge calculation and storage medium |
| CN115965265A (en) * | 2022-11-14 | 2023-04-14 | 国网山西省电力公司大同供电公司 | Electric power energy efficiency improving method and system based on average electricity price |
| CN116485441A (en) * | 2023-06-20 | 2023-07-25 | 深圳市拓普瑞电子有限公司 | Multi-equipment energy consumption record analysis method, system, terminal and storage medium |
| CN116780536A (en) * | 2023-08-23 | 2023-09-19 | 山东未来互联科技有限公司 | Energy consumption prediction method and system based on machine learning |
-
2023
- 2023-12-28 CN CN202311835305.XA patent/CN117495056A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107203826A (en) * | 2017-05-19 | 2017-09-26 | 浙江大学 | A kind of power program optimization method of industrial user |
| CN110298509A (en) * | 2019-06-28 | 2019-10-01 | 佰聆数据股份有限公司 | A kind of big industrial trade Electric optimization of combination short-term load forecasting |
| CN111445205A (en) * | 2020-03-25 | 2020-07-24 | 天纳能源科技（上海）有限公司 | Electrical load control system |
| CN113095595A (en) * | 2021-05-06 | 2021-07-09 | 广东鹰视能效科技有限公司 | Energy efficiency optimization method and system based on power distribution operation and maintenance |
| CN115730790A (en) * | 2022-11-14 | 2023-03-03 | 四川新仑科技有限公司 | Charging configuration method, device and equipment based on edge calculation and storage medium |
| CN115965265A (en) * | 2022-11-14 | 2023-04-14 | 国网山西省电力公司大同供电公司 | Electric power energy efficiency improving method and system based on average electricity price |
| CN116485441A (en) * | 2023-06-20 | 2023-07-25 | 深圳市拓普瑞电子有限公司 | Multi-equipment energy consumption record analysis method, system, terminal and storage medium |
| CN116780536A (en) * | 2023-08-23 | 2023-09-19 | 山东未来互联科技有限公司 | Energy consumption prediction method and system based on machine learning |
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