EP3167412A1 - Procédé et dispositif de pronostique de la variation temporelle d'une consommation électrique d'un ensemble d'habitations - Google Patents

Procédé et dispositif de pronostique de la variation temporelle d'une consommation électrique d'un ensemble d'habitations

Info

Publication number
EP3167412A1
EP3167412A1 EP15736242.7A EP15736242A EP3167412A1 EP 3167412 A1 EP3167412 A1 EP 3167412A1 EP 15736242 A EP15736242 A EP 15736242A EP 3167412 A1 EP3167412 A1 EP 3167412A1
Authority
EP
European Patent Office
Prior art keywords
forecast
characteristic
residential
load
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP15736242.7A
Other languages
German (de)
English (en)
Inventor
Heinz HANEN
Frank Diedrich
Valentin BERTSCH
Hannes SCHWARZ
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Evohaus Irq GmbH
Original Assignee
Evohaus GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Evohaus GmbH filed Critical Evohaus GmbH
Publication of EP3167412A1 publication Critical patent/EP3167412A1/fr
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates to a method and an apparatus for predicting the time course of a power requirement of a residential complex with one or a plurality of residential units with a respective plurality of power consumers, wherein the housing complex has a common power supply, which is subject to a time-variable rate.
  • a residential complex within the meaning of this document is understood as one or more residential units having a common power supply and electricity billing.
  • Residential property in the sense of this document includes urban quarters, ie residential complexes with spatially or legally separate residential units or groups of residential units that share facilities.
  • the object of the invention is to enable a reduction in electricity costs without negative influences on the daily routine and the living habits of the residents of the housing complex.
  • the coverage ratio is 100%, if the entire (predicted) electricity consumption of the residential complex can be covered by the photovoltaic system in the case of strong solar radiation. Consequently, the coverage ratio is 0% if the photovoltaic system, z. B. in the dark, can make any contribution to meet the electricity needs of the housing complex. In between, all values are possible, depending on the forecast production and consumption.
  • the time variability of electricity costs arises from the fact that the cost of a reference unit for self-generated photocurrent are generally different than for electricity from central generation, which is obtained via the public grid. If the cost of photocurrent is lower than for electricity from the public grid, it is advantageous for the residents in the condominium to shift as much of their power reference in such periods where there is sufficient photocurrent available.
  • Such a task is known as a load shift.
  • the problem is to switch electrical loads to perform the tasks you expect while keeping energy costs as low as possible.
  • the electricity prices must be household appliance relevant time corridor be at least approximately predictable.
  • the price of mains electricity is known. This is generally the case for housing estates.
  • the grid current price is always the same regardless of the time.
  • the invention also includes the case that the grid current price is variable over time. It just has to be predictable.
  • the future mixed electricity price is calculated by combining the predicted future grid electricity price with the photocurrent price taking into account the predicted future coverage.
  • the present invention addresses the problem of predicting residential power consumption.
  • DE 10 2011 051 074 A1 describes a method for controlling the use of energy taking into account a forecast of the future energy demand of one or more energy consumers in a home network.
  • a system for predicting the demand for electrical energy is known from US 5,178,237
  • a method for distributing energy on a power supply network is known from DE 19 853 347 AI.
  • the future energy demand is estimated based on self-reported consumer information about their desired power consumption.
  • the basis for the load shift may be a direct regulation by the energy supplier, but also an indirect load influencing by means of tariff incentives. Based on this, load management procedures in the household sector can be found in response to the volatility of decentralized generation capacity with a particular focus on indirect, incentive-based load management using tariff incentives in [Kamper, A .: Decentralized load management to compensate for short-term deviations in the electricity grid; Diss., Düsseldorf 2010] and in [Hillemacher, L .; Jochem, P .; Fichtner, W .: decision support for load management; in: Renatus, F .; Kunze, R .; Karschin, I .; Geldermann, J .; Fichtner, W.
  • DE 10 2012 105 404 A1 describes systems and methods for predicting energy consumption which, based on a measured local energy usage range profile and on demographic information relating to a new usage area, enable usage prognosis for the new usage area.
  • DE 10 2012 103 081 A1 describes an optimized load management method for optimizing a time profile of a consumption of electrical power by a group of different consumers with regard to an offer of electrical power, which comprises electric power of at least one wind or solar power generator and power, which is bidirectionally exchanged with a storage for electrical energy and / or a public power grid.
  • Characteristic time profiles of the consumption of electrical power of the individual consumers are determined, and a forecast of the time course of the supply of electrical power is created by the at least one power generator for a future period. Based on this, a plan for allocating electrical power to consumers for the future period is created.
  • DE 10 2010 025 095 AI describes an apparatus and a method for consumption monitoring of resources, such as electricity, water, natural gas or district heating. solution
  • the present invention provides a method for predicting the time course of a power requirement of a residential complex with one or a plurality of residential units with a respective plurality of power consumers, wherein the housing complex has a common power supply, which is subject to a time-variable rate according to claim 1 and a corresponding device according to claim 14.
  • the inventors have recognized that in the boundary conditions described here, namely that as the contractual partner of the energy supplier not individual residential units, but an entire residential complex occurs, can be compared to the prior art improved forecast create. This is achieved because, given a sufficiently large number of housing units within the housing estate, deviations in the load profile of individual residential units in the overall view of the housing estate do not affect the prognosis quality to the same extent as they would for the individual consideration. This offers the possibility of creating a prognosis that can better reflect housing-specific conditions than even regionally adapted standard load profiles could do. By the total number of
  • Residential units can be expected to have a sufficiently accurate forecasting quality.
  • the inventors have recognized that it is advantageous if the selection of the historical load paths used for the prognosis is constantly updated and thus adapted to changing circumstances, such as the resident structure of the housing estate.
  • the inventors have further recognized that in the prognosis the weighting of the used historical data according to their age is a useful degree of freedom and can be optimized for the forecasting process.
  • the invention described here solves the problem of approximately predicting the power consumption for residential complexes, taking into account the individual characteristics of a particular housing estate. It therefore provides more accurate predictions than prior art predictions. It thus enables a better load shift and thus a greater reduction in electricity costs in the housing complex, because it allows the housing to shift electrical loads at times with lower electricity costs and to predict these times with greater accuracy.
  • Fig. 1 is a typical residential unit in a residential complex
  • Fig. 2 shows a typical residential complex and the time course of the electricity consumed by the residential complex
  • 3 shows an example of how known daily load cycles of the housing estate are assigned to one or more characteristic parameters
  • FIG. 5 shows an exemplary embodiment of a computer on the basis of a block diagram
  • Fig. 6 the preparation of a forecast for a future load profile according to another embodiment of the invention.
  • Fig. 1 shows a typical residential unit (101) in a residential complex.
  • the photovoltaic systems (102) can be exemplary on the roofs of the
  • Residential units are located. However, they can also be arranged at other locations of the housing complex, for example on roofs of motor vehicle parking spaces or in the outdoor area.
  • electrical consumers for example washing machine (107) and stove (104).
  • the electrical power consumption of the residential unit is measured by an electricity meter (105). ).
  • the electricity meter (105) transmits the time profile of the consumed electricity via a suitable interface and data input (502) to a processor (501) (see FIG.
  • the residential unit also houses water consumers (103).
  • the water meter (106) transmits the time profile of the consumed water of a residential unit via a suitable interface and data input (502) to a processor (501) (see Fig. 5).
  • FIG. 2 shows a typical residential complex and the time course of the electricity consumed the condominium.
  • a residential complex (201) consists of one or more residential units (101).
  • the one or more photovoltaic system (s) of the housing complex (201) are interconnected and their common power output is measured by a photocurrent counter (203).
  • the different yields of the individual photovoltaic systems of the residential complex can be added.
  • the electricity consumption of all residential units is recorded by a housing electricity meter (202) in total.
  • the residential electricity meter (202) may be dispensed with, and the total power consumption of the housing complex may be determined by adding the various revenues of the individual home meters (105) as well as additional general electricity meters in the condominium. Without limiting the general public, it is assumed in the further course of the description that the electricity consumption of all residential units and the common current are covered in total by a residential electricity meter (202).
  • the residential electricity meter (202) transmits the time history of the used electricity (204) via a suitable interface and data input (502) to a processor (501) (see Fig. 5).
  • the temporal course of the consumed current is also called load profile in the following.
  • the prognosis according to the invention of a future load profile in the housing estate is made by the use of historical, that is to say known, load courses of the housing complex.
  • the prognosis is determined by assigning one or more characteristic parameters to the known daily load cycles of the residential complex which can be at least approximately predicted and which have an influence on the load profile. This will be explained with reference to an example and Fig. 3.
  • the well-known historical load of a housing estate for the exemplarily selected period 2.3.2013 - 5.3.2013 (308) is shown graphically as energy consumption per 15 minutes.
  • the power consumption values for all quarter-hourly time intervals are shown in this four-day period. This illustration is exemplary. Other representations of the time history of energy consumption are also possible, for example indicating energy consumption for intervals shorter than a quarter of an hour, or for intervals longer than a quarter of an hour, for example one hour.
  • day 2.3.2013 is assigned the 3 characteristic parameters winter, sun, working day (302).
  • Fig. 3 (303), (304).
  • the (302) - (304) selected characteristic parameters are called standard type days and are known from VDI 4655 ["reference load profiles of single and multi-family houses for the use of CHP systems", VDI directive VDI 4655, May 2008] that each of these combinations occurs sufficiently frequently in the course of the year, and the prediction of the future characteristic parameters is only safety of the weather forecast (cloudy or sunny), and may be considered at least approximately predictable given the state of the art for weather forecasts.
  • the characteristic parameters summer / winter, working day / holiday, sun / clouds also have a significant influence on the load profile.
  • FIG. 4 illustrates the preparation of a forecast for a future load profile according to an embodiment of the invention.
  • the creation according to the invention of a prognosis (404) for the future load profile in the time period T (405) is carried out in such a way that suitable parameters are initially determined. These can be, for example, the above-mentioned standard type days.
  • suitable parameters can be, for example, the above-mentioned standard type days.
  • the values for the characteristic parameters for this period T are then predicted.
  • the values 'winter - sun - working day' could be used if typical days were selected.
  • the last prognosis for a period with this characteristic set 1 is then selected (401). By way of example, this would be the last forecast with the values 'winter - sun - working day'. Furthermore, the last period T with known consumption data for which the characteristic set 1 applies is selected (402). By way of example, these would be the last consumption data with the values, winter - sun - working day '.
  • the method according to which the computer determines this prognosis can be optimized individually.
  • the method may be chosen such that the data of the last prediction (401) and the data of the last known load profile (402) are arithmetically averaged by adding the values to each individual data point and dividing by two.
  • the procedure can also be defined differently and the data of the last one Forecast (401) may provide weighting other than the last known load history (402). In this way, it is possible to vary the range of the historical data used in the past: the higher the weight of the data of the last forecast (401) compared to the weight of the data of the last known load profile (402) is chosen, the greater the reach into the past. It is advantageous to make the weighting variable and to optimize continuously so that the current forecast deviates as little as possible from the actual consumption values.
  • characteristic parameters are used for this purpose.
  • the invention encompasses not only the use of type days according to VDI 4655 as characteristic parameters, but also characteristic parameters of greater generality. It has been recognized by the inventors that the characteristic parameters must be selected such that they have a significant influence on the load profile, are themselves at least approximately well predictable and occur sufficiently frequently in each combination during the course of the year.
  • the characteristic values are chosen as follows: 'very cold' / temperature ⁇ -2 °, 'cold' / temperature between -2 ° and 5 °, 'medium' / temperature between 5 ° and 10 °,, warm Temperature between 10 ° and 20 ° and 'very warm' / temperature> 20 °.
  • Characteristic parameters can be selected in another embodiment of the invention, for example, depending on the number of people living in the housing. The number of registered persons living in a specific housing estate will vary over the course of the year, depending on whether some residents are on holiday or guests are visiting the condominium, etc.
  • the electricity consumption generally depends on the number of people living in a condominium
  • Incorporating the number of people living in a housing estate into the electricity consumption forecast will improve their accuracy.
  • the prediction of the number of persons living in a residential complex can be done, for example, by the residents themselves providing information and providing the computer (403) with corresponding data.
  • the inventors have recognized that in this case a prognosis of the number of persons living in a residential complex can be given by evaluating non-electrical consumption data.
  • the inventors have further recognized that, for example, the number of toilet flushes in a day is closely correlated to the number of people living in a housing estate, and that the number of toilet flushes can be estimated by evaluating the temporal water consumption.
  • another suitable non-electrical consumption quantity is, for example, the heat consumption, or the temperature of a residential unit or in a certain room of the residential unit. It has been found by the inventors that the temporal heat consumption or the temperature can be correlated with the number of inhabitants in a residential unit. It was recognized that residents before long leave their apartment lower the target temperature, so that it can be concluded from the measured temperature or from the measured heat consumption on the number of residents. It has further been recognized that it is also possible to deduce the unknown power consumption directly from the predictable temporal heat consumption or the predictable temperature, and that therefore characteristic characteristics derived from the temporal heat consumption or the temperature can be used.
  • 'T low' it is to be expected that there will be no residents in the housing unit and it can be predicted that this will most likely also apply in the near future. It can thus be used to modify the prediction of the power consumption of the housing estate so that a correspondingly lower household electricity is predicted for the uninhabited housing unit (s).
  • the prediction is made for each residential unit using the method illustrated in FIG.
  • the forecast is created for each residential unit of the housing complex.
  • the characteristic parameter used is the temperature with the two possible values 'T high' and 'T low'.
  • the forecast of the power consumption of the entire residential complex results in this embodiment by adding the forecasts of all residential units in the residential complex.
  • the accuracy of the forecasting method described above can be further improved if the total electricity consumption of the housing estate is divided into two parts and these two proportions are forecast separately: a share for the production of heat energy and another share for all other electricity consumers in the condominium.
  • the prognosis is carried out in parallel and independently for the two components 'electricity for heat generation' and 'residual electricity'.
  • the forecast for the "residual current" takes place according to the method described above (FIG. 6).
  • the forecast for the 'power for heat generation' can also be made according to the method described above (FIG. 6). But it can also be done by direct calculation, without recourse to historical data, for example from climate data and building simulation. The total power consumption then results as the sum of the two shares.
  • Fig. 5 shows an embodiment of a computer (403) based on a block diagram.
  • This includes a processor (501).
  • processor (501) executes program instructions. s stored in program memory (504), and stores, for example, intermediate results or the like in the data memory (503).
  • Program memory (504) and / or main memory (503) may be used by the processor (501) to store data, such as forecast data (401) or consumption data (308), (402).
  • Program instructions stored in program memory (504) relate in particular to the determination of at least one of said forecasts.
  • the program instructions may, for example, be comprised of a computer program stored in program memory (504) or loaded into program memory (504), for example from a computer program product, in particular a computer-readable storage medium, or via a network.
  • the processor (501) receives data via interface and data input (502).
  • Data is, for example, consumption data (308), (402) or prognosis data of characteristic parameters (302) - (307).
  • the processor (501) generates new data and outputs it via interface and data output (505).
  • the output data is visualized / displayed (507) and / or forwarded to a consumer controller (506).
  • a consumer controller may, for example, use the current forecast data to switch power consumers in such a way that the lowest possible electricity costs arise.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

L'invention concerne un procédé et un dispositif permettant de pronostiquer la variation temporelle d'une consommation électrique d'un ensemble d'habitations avec une unité d'habitation ou une pluralité d'unités d'habitation avec une pluralité respective de consommateurs d'électricité, l'ensemble d'habitations comprenant une alimentation électrique commune qui est soumise à un tarif horaire variable. Le procédé est configuré de manière à : établir un pronostic de la consommation d'électricité (404) de l'ensemble d'habitations (201) pour une période temporelle future d'une durée prédéfinie T et l'actualiser en permanence. Un transfert de charge pour la réduction des coûts d'électricité peut être déterminé en fonction d'un pronostic actuel de la consommation d'électricité (404), un pronostic en question de la consommation d'électricité (404) emploie des données de courbe de charge détectées (402) de l'ensemble d'habitations (202) qui ont été formées par la détection de la variation temporelle de la consommation totale de l'ensemble d'habitations dans la période temporelle de la durée prédéfinie T, un pronostic actuel (404) pour la période temporelle future de la durée prédéfinie T est calculé sur la base d'un pronostic précédent (401) et de données d'une courbe de charge (402) précédente détectée, et une ou plusieurs grandeurs caractéristiques (305, 306, 307), dont les pronostics sont au moins connus par approximation et qui ont une influence sur les évolutions de consommation, sont attribuées en tant qu'ensemble de valeurs des grandeurs caractéristiques (406) à des pronostics et à des évolutions de consommation détectées, et un pronostic actuel (404) pour la période temporelle future de la durée prédéfinie T est calculé sur la base d'un pronostic précédent (401) avec le même ensemble de valeurs des grandeurs caractéristiques (406) et d'une courbe de charge précédente détectée (402) avec le même ensemble de valeurs des grandeurs caractéristiques (406).
EP15736242.7A 2014-07-08 2015-07-08 Procédé et dispositif de pronostique de la variation temporelle d'une consommation électrique d'un ensemble d'habitations Ceased EP3167412A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102014010117.7A DE102014010117A1 (de) 2014-07-08 2014-07-08 Prognose- und Steuerungssystem für den Strombezug von Haushalten
PCT/EP2015/065588 WO2016005441A1 (fr) 2014-07-08 2015-07-08 Procédé et dispositif de pronostique de la variation temporelle d'une consommation électrique d'un ensemble d'habitations

Publications (1)

Publication Number Publication Date
EP3167412A1 true EP3167412A1 (fr) 2017-05-17

Family

ID=53540745

Family Applications (1)

Application Number Title Priority Date Filing Date
EP15736242.7A Ceased EP3167412A1 (fr) 2014-07-08 2015-07-08 Procédé et dispositif de pronostique de la variation temporelle d'une consommation électrique d'un ensemble d'habitations

Country Status (4)

Country Link
US (1) US11195105B2 (fr)
EP (1) EP3167412A1 (fr)
DE (1) DE102014010117A1 (fr)
WO (1) WO2016005441A1 (fr)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9645596B1 (en) * 2016-11-23 2017-05-09 Advanced Microgrid Solutions, Inc. Method and apparatus for facilitating the operation of an on-site energy storage system to co-optimize battery dispatch
CN108022014B (zh) * 2017-12-05 2021-06-01 国网山东省电力公司济南供电公司 一种电力系统负荷预测方法及系统
US11455695B2 (en) * 2019-12-16 2022-09-27 Saudi Arabian Oil Company System and method for modelling and forecasting electricity demand
CN110988556B (zh) * 2019-12-20 2022-03-29 上海市建筑科学研究院有限公司 一种非运行时段建筑照明插座支路用能诊断方法
DE102022116001A1 (de) 2022-06-28 2023-12-28 WoodenValley UG (haftungsbeschränkt) Verfahren und system zur dynamischen bestimmung von gebäudeemissionen

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102011051074A1 (de) * 2011-06-15 2012-12-20 Rwe Effizienz Gmbh Steuern der Verwendung von Energie

Family Cites Families (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04131600A (ja) * 1990-09-19 1992-05-06 Hitachi Ltd 都市エネルギーシステム
JPH1080062A (ja) * 1996-09-05 1998-03-24 Fuji Electric Co Ltd 日負荷曲線予測方法
DE19853347A1 (de) 1998-11-19 2000-05-25 Abb Research Ltd Verfahren zum Verteilen von Energie auf einem Stromversorgungsnetz
JP2002041714A (ja) * 2000-07-21 2002-02-08 Hitachi Ltd 電力の需要予測サービス方法およびシステム
US8706650B2 (en) * 2009-01-14 2014-04-22 Integral Analytics, Inc. Optimization of microgrid energy use and distribution
DE102010025095A1 (de) * 2009-06-29 2011-02-03 Polyrack Electronic-Aufbausysteme Gmbh Vorrichtung und Verfahren zur Verbrauchsüberwachung, insbesondere des Verbrauchs von Ressourcen und Verwendung einer solchen Vorrichtung in einem Energiecontrollingsystem
JP5618501B2 (ja) * 2009-07-14 2014-11-05 株式会社東芝 需要予測装置、プログラムおよび記録媒体
CN102985936A (zh) * 2010-03-08 2013-03-20 松下电器产业株式会社 开始利用区间预测装置及开始利用区间预测方法
DE102010027726A1 (de) 2010-04-14 2012-05-10 Bayerische Motoren Werke Aktiengesellschaft Verfahren zur Bereitstellung von elektrischer Energie
US8538593B2 (en) * 2010-07-02 2013-09-17 Alstom Grid Inc. Method for integrating individual load forecasts into a composite load forecast to present a comprehensive synchronized and harmonized load forecast
US8392031B2 (en) * 2011-02-28 2013-03-05 General Electric Company System and method for load forecasting
US9244444B2 (en) * 2011-03-07 2016-01-26 Callida Energy Llc Systems and methods for optimizing energy and resource management for building systems
DE102012103081B4 (de) * 2011-04-08 2021-12-30 Sma Solar Technology Ag Optimiertes Lastmanagement
JP5353957B2 (ja) * 2011-06-14 2013-11-27 株式会社デンソー 電力供給システム
US20120330472A1 (en) * 2011-06-21 2012-12-27 General Electric Company Power consumption prediction systems and methods
US8751054B2 (en) * 2011-09-02 2014-06-10 Sharp Laboratories Of America, Inc. Energy prediction system
JP5492848B2 (ja) * 2011-09-20 2014-05-14 株式会社日立製作所 電力需要予測システムおよび方法
JP5877346B2 (ja) * 2011-10-24 2016-03-08 パナソニックＩｐマネジメント株式会社 負荷制御装置、プログラム、負荷制御システム
AU2012344680B2 (en) * 2011-11-29 2017-10-12 Generac Power Systems, Inc. Method and system for forecasting power requirements using granular metrics
US9489701B2 (en) * 2012-11-06 2016-11-08 Ali Emadi Adaptive energy management system
US9639904B2 (en) * 2012-12-11 2017-05-02 Opterra Energy Services, Inc. Systems and methods for minimizing energy costs for a power consumption system that has access to off-grid resources
US9477240B2 (en) * 2013-04-29 2016-10-25 Eaton Corporation Centralized controller for intelligent control of thermostatically controlled devices
US9576327B2 (en) * 2013-06-06 2017-02-21 International Business Machines Corporation Managing time-substitutable electricity usage using dynamic controls
WO2015045552A1 (fr) * 2013-09-27 2015-04-02 日本電気株式会社 Dispositif de gestion de cellule de stockage d'énergie, cellule de stockage d'énergie, procédé pour gérer une cellule de stockage d'énergie et programme

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102011051074A1 (de) * 2011-06-15 2012-12-20 Rwe Effizienz Gmbh Steuern der Verwendung von Energie

Also Published As

Publication number Publication date
WO2016005441A1 (fr) 2016-01-14
DE102014010117A1 (de) 2016-01-14
US11195105B2 (en) 2021-12-07
US20170161623A1 (en) 2017-06-08

Similar Documents

Publication Publication Date Title
DE102004030846B4 (de) System zur zentralen Steuerung für Klimaanlagen und Verfahren zum Betreiben desselben
DE102012103081B4 (de) Optimiertes Lastmanagement
EP2834573B1 (fr) Système de fourniture de chaleur et procédé permettant de faire fonctionner ledit système
EP3167412A1 (fr) Procédé et dispositif de pronostique de la variation temporelle d'une consommation électrique d'un ensemble d'habitations
DE102016015503B4 (de) Verfahren zur Temperierung mindestens eines Gebäuderaums mit Prognose und Optimierung der Energieaufnahme
DE112010003256T5 (de) System und Verfahren für Energieverbrauchsmanagement
DE102012217636A1 (de) Aktive überwachung und kontrolle häuslicher lasten
DE102012023486A1 (de) Kraftwärmekopplungssystem
DE202022106297U1 (de) Intelligentes System für Energiebedarfsprognosen und nachhaltiges Energiemanagement durch maschinelles Lernen und künstliche Intelligenz
DE102018116845A1 (de) Verfahren zum Betreiben einer Wärmepumpe
EP3561983A1 (fr) Procédé de fonctionnement d'un système d'alimentation en énergie
EP2843788A2 (fr) Procédé destiné au fonctionnement d'un système de centrale
EP1220412B1 (fr) Procédé et dispositif pour la commande et le contrôle de la consommation d'énergie électrique
EP0268697A1 (fr) Procédé et système pour la réduction de pointes de puissance pour des puissances de distribution et d'évacuation attachées à des conduites
WO2013149899A1 (fr) Procédé de détermination de points de mesure dans des réseaux à basse tension et sous-système de gestion de réseau servant à mettre en œuvre ce procédé
CH711189A2 (de) System zur Stromtarifsteuerung und Stromversorgung für ein Gebäude, Verfahren zur Stromtarifsteuerung für ein Gebäude und Abrechnungsverfahren für die Stromversorgung eines Gebäudes.
EP3101749B1 (fr) Procédé de commande d'un consommateur de courant local
DE102012216369A1 (de) Elektrische einrichtung zum anschluss an ein infrastrukturstromnetz und verfahren
EP3167413A1 (fr) Procédé de gestion d'énergie et dispositif de gestion d'énergie pour un ensemble d'habitations avec une ou plusieurs unité d'habitation ou pour un quartier urbain
DE102014207077A1 (de) Verfahren zum Steuern einer elektrischen Leistung
DE102014211256A1 (de) Steuervorrichtung und Verfahren zum Steuern eines Gebäudeautomationssystems
DE102015010941A1 (de) Netzstabilisierende Stromversorgung
DE102013008870A1 (de) Elektrizitätsnetz - Stabilisierung
EP2851666A1 (fr) Procédé et dispositif de détermination du degré d'utilisation de l'énergie d'une installation énergétique
DE102013226761A1 (de) Verfahren und Vorrichtung zum Auslegen einer Fotovoltaik-Anlage

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20170208

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
RAP1 Party data changed (applicant data changed or rights of an application transferred)

Owner name: EVOHAUS IRQ GMBH

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20180214

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

APBK Appeal reference recorded

Free format text: ORIGINAL CODE: EPIDOSNREFNE

APBN Date of receipt of notice of appeal recorded

Free format text: ORIGINAL CODE: EPIDOSNNOA2E

APBR Date of receipt of statement of grounds of appeal recorded

Free format text: ORIGINAL CODE: EPIDOSNNOA3E

APAF Appeal reference modified

Free format text: ORIGINAL CODE: EPIDOSCREFNE

APAF Appeal reference modified

Free format text: ORIGINAL CODE: EPIDOSCREFNE

REG Reference to a national code

Ref country code: DE

Ref legal event code: R003

APBT Appeal procedure closed

Free format text: ORIGINAL CODE: EPIDOSNNOA9E

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN REFUSED

18R Application refused

Effective date: 20230427