A novel approach in household electricity consumption forecasting

In this paper, we devise a comprehensive model to forecast the energy consumption of the bulk energy consumers. The behavior of the bulk energy consumers is different and consumers has their own consumption patterns; therefore, we segment the bulk energy consumers into two clusters: low-consumption and high-consumption. Individual predictive models are made for each segment. Box and Jenkins and regression models are utilized to forecast energy consumption. We use the time series to forecast energy usage and as a novel method, we combine the time series results with weather data to form our regression model. Our results shows decrease in error and extension of forecasting period. We use the Akaike Information Criterion (AIC) for model selection among a finite set of models.