Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

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.

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

“…The publications encompass authors from various countries, with the most prevalent nationalities among the authors being China (109), the USA (32), India (31), Saudi Arabia (30), Spain (27), the United Kingdom (23), South Korea (21), Italy (11), Japan (11), and Canada (10), which comprise the top ten contributors in this category. In particular, South Korea stands out as the country with the highest number of citations, with an accumulated total of 148 citations for its contributions in this area.…”

“…The analysis of these articles will provide valuable insights into the advancements and challenges in accurately forecasting energy generation from renewable sources, enabling better energy planning and management in Smart City environments. In [31], the authors developed a method based on non-parametric regression models that forecasts the demand and generation of energy with information provided by smart meters. Another application for forecasting purposes can be reviewed in [33], where a physics-informed AI is applied that forecasts wind power generation, with information on a wind farm in China and ML methods.…”