Dynamic Regression Prediction Models for Customer Specific Electricity Consumption

We have developed a conventional benchmark model for the prediction of two days of electricity consumption for industrial and institutional customers of an electricity provider. This task of predicting 96 values of 15 min of electricity consumption per day in one shot is successfully dealt with by a dynamic regression model that uses the Seasonal and Trend decomposition method (STL) for the estimation of the trend and the seasonal components based on (approximately) three years of real data. With the help of suitable R packages, our concept can also be applied to comparable problems in electricity consumption prediction.