Day-ahead forecasting of residential electric power consumption for energy management using LSTM encoder-decoder model

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand based on the Long Short-Term Memory encoder-decoder architecture and considering past and future exogenous inputs. The proposed approach is tailored for use by energy management systems and its performance was validated accordingly. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the mean daily error up to 8% Mean Absolute Scaled Error.