Electricity demand forecasting of single residential units

The use of demand side Advanced Metering Infrastructures in power distribution grids permits to collect huge amount of valuable information about residential energy expenditure. Electric utilities are already exploiting similar information for Forecasting Algorithms and Demand Side Management that have demonstrated to reduce the overall electricity demand. To push further this “green” trend toward the realization of Smart Grids, we propose to apply the forecasting techniques also for electricity demand of the residential users. Exponential smoothing forecasting has been demonstrated to be effective to analyze and to provide trends for higher scale (National or Regional level) of the demand. We moved and tested the approach to residential users and assessed the performance when data have high time variability. Two different datasets have been used and the accuracy of the forecasting has been compared to the results when aggregated national data are used. Our tests show encouraging results, even if the prediction’s accuracy is still lower when dealing with single users confirming that pre-processing and filtering of the collected data is fundamental to achieve good predictions.