Engineering Commons^™

Lstm Forecasts For Smart Home Electricity Usage, Rosemary E. Alden, Huangjie Gong, Cristinel Ababei, Dan M. Ionel

Power and Energy Institute of Kentucky Faculty Publications

With increasing of distributed energy resources deployment behind-the-meter and of the power system levels, more attention is being placed on electric load and generation forecasting or prediction for individual residences. While prediction with machine learning based approaches of aggregated power load, at the substation or community levels, has been relatively successful, the problem of prediction of power of individual houses remains a largely open problem. This problem is harder due to the increased variability and uncertainty in user consumption behavior, which make individual residence power traces be more erratic and less predictable. In this paper, we present an investigation of …