The successful implementation of Renewable Energy Communities(RECs)involves maximizing the self-consumption within a community,particularly in regulatory contexts in which shared energy is incentivized.In many countri...The successful implementation of Renewable Energy Communities(RECs)involves maximizing the self-consumption within a community,particularly in regulatory contexts in which shared energy is incentivized.In many countries,the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users(e.g.,residential and commercial users)makes the design of a new energy community a challenging task.This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data(i.e.,billed energy),with the aim of estimating the energy shared by RECs.The proposed methodology involves three phases:first,identifying the typical load patterns of residential users through k-Means clustering,then implementing a Random Forest algorithm,based on monthly energy bills,to identify typical load patterns and,finally,reconstructing the hourly electrical load profile through a data-driven rescaling procedure.The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant.The Normalized Mean Absolute Error(NMAE)and the Normalized Root Mean Squared Error(NRMSE)were evaluated over an entire year and whenever the energy was shared within the REC.The Relative Absolute Error was also measured when estimating the shared energy at both a monthly(MRAE)and at an annual basis.(RAE).A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04%,an NRMSE of 26.17%,and errors of 18.34%and 23.87%during shared energy timeframes,respectively.Furthermore,our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31%and 0.12%,respectively.展开更多
基金the project“Network 4 Energy Sustainable Transition-NEST”,Project code PE0000021Concession Decree No.1561 of 11.10.2022 adopted by Ministero dell’Universit`a e della Ricerca(MUR),CUP E13C22001890001+1 种基金funded under the National Recovery and Resilience Plan(NRRP),Mission 4 Component 2 Investment 1.3-Call for tender No.341 of 15.03.2022 of MURfunded by the European Union-NextGenerationEU.
文摘The successful implementation of Renewable Energy Communities(RECs)involves maximizing the self-consumption within a community,particularly in regulatory contexts in which shared energy is incentivized.In many countries,the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users(e.g.,residential and commercial users)makes the design of a new energy community a challenging task.This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data(i.e.,billed energy),with the aim of estimating the energy shared by RECs.The proposed methodology involves three phases:first,identifying the typical load patterns of residential users through k-Means clustering,then implementing a Random Forest algorithm,based on monthly energy bills,to identify typical load patterns and,finally,reconstructing the hourly electrical load profile through a data-driven rescaling procedure.The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant.The Normalized Mean Absolute Error(NMAE)and the Normalized Root Mean Squared Error(NRMSE)were evaluated over an entire year and whenever the energy was shared within the REC.The Relative Absolute Error was also measured when estimating the shared energy at both a monthly(MRAE)and at an annual basis.(RAE).A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04%,an NRMSE of 26.17%,and errors of 18.34%and 23.87%during shared energy timeframes,respectively.Furthermore,our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31%and 0.12%,respectively.
