Smart meter data can be used for various purposes within smart grids, including residential energy applications, such as Home Energy Management Systems (HEMS) and Battery Energy Management Systems (BEMS). Considering the low feed-in tariffs for rooftop photovoltaic (PV) and increasing customer electricity prices, maximizing PV selfconsumption becomes a key objective for these energy management systems.
This paper analyses the impacts of household electricity load consumption profile and PV size on PV self-consumption. A clustering model has been developed to classify households according to their daily load and generation profiles and PV size. The study is then extended to analyse the influence of different seasons on the self-consumption forecast. The results show that the clustering model can guide HEMS and BEMS in deciding more accurate strategies for forecasting day-ahead PV self-consumption.
A method for classifying households to help forecasting their PV electricity self-consumption patterns (500009 PDF)