Short term electricity forecasting based on user behavior from individual smart meter data

Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is twofold: (1) we deal with short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level what fits into the stream of Residential Power Load Forecasting (RPLF) methods; (2) we utilized a set of household behavioral data which significantly improved the forecasts accuracy.