Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting

The electric grid is changing. With the smart grid the demand response (DR) programs will hopefully make the grid more resilient and cost efficient. However, a scheme where consumers can directly participate in demand management requires new efforts for forecasting the electric loads of individual consumers. In this paper we try to find answers to two main questions for forecasting loads for individual consumers: First, can current short term load forecasting (STLF) models work efficiently for forecasting individual households? Second, do the anthropologic and structural variables enhance the forecasting accuracy of individual consumer loads? Our analysis show that a single multi-dimensional model forecasting for all houses using anthropologic and structural data variables is more efficient than a forecast based on traditional global measures. We have provided an extensive empirical evidence to support our claims.

► We forecast energy load of individual houses for smart grid applications.
► We use anthropologic and structural data of houses to augment the forecasting.
► We propose a different modeling paradigm for constructing succinct models.
► Combination of richer data and proposed modeling increase accuracy by up to 50%.
► This is useful for future demand side management and demand response applications.