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Title Forecast Of Area-Scale Behaviours Of Behind-The-Metre Solar Power And Load Based On Smart-Metering Net Demand Data
ID_Doc 26824
Authors Miyasawa A.; Akira S.; Fujimoto Y.; Hayashi Y.
Year 2023
Published IET Smart Cities, 5, 1
DOI http://dx.doi.org/10.1049/smc2.12050
Abstract Local energy self-sufficiency, in which the supply and demand of electricity are controlled such that the generated power from distributed energy resources (DERs) is consumed locally based on a power supply-and-demand forecast, mitigates the burden on the power system and contributes to the efficient use of DERs in smart cities. However, widely available smart metres cannot measure behind-the-metre pure demand and generation from prosumers. Pure demand and generation forecasts without additional metering contribute to advanced supply-and-demand control in smart cities, including demand response. This study proposes a method of forecasting spatio-temporal behaviours of behind-the-metre pure demand and generation by focussing on the information of net demand distribution observable from the smart metres; the proposed method initially predicts the spatio-temporal net demand distribution with a combined forecaster based on the persistence and non-parametric regression models, and then separately estimates the behind-the-metre pure demand and generation by using demand forecast result of neighbouring pure-consumers extracted by considering the area-scale behaviours of the smart metering data. The simulation results demonstrate that the proposed method provides accuracy comparable to forecasts conducted by directly measuring pure demand and generation, without requiring the installation of additional metres. © 2023 The Authors. IET Smart Cities published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Author Keywords Artificial Intelligence; city brain and smart cities metrics; Data Analytics and Machine Learning; Data Structures; distributed power generation; power meters; smart cities


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