Short-Term Electric Demand Forecasting for the Residential Sector: Lessons Learned from the RESPOND H2020 Project

Abstract

RESPOND proposes an Artificial Intelligent (AI) system to assist residential consumers that would like to make use of Demand Response (DR) and incorporate it into their energy management systems. The proposed system considers the forecast energy consumption based on the data acquired. This work compares the results obtained by different forecasting methods using the Root Mean Square Error (RMSE) as a measure of the forecast performance. The ARIMA, Linear Regression (LR), Support Vector Regression (SVR) and k-Nearest Neighbors (KNN) models are tested, and it is concluded that the results achieved with the KNN obtain a better fit. In addition to obtaining the lowest RMSE, KNN is the algorithm that spends less time in obtaining the forecasts.