Random forests model for one day ahead load forecasting

Abstract

Short term load forecasting is one of the most important tasks for power suppliers, and it is getting more important with deregulation of electricity market and emergence of smart grids. This paper proposes a load prediction model of one day ahead with resolution of one hour, using regression random forests. With information about season, temperature, type of the day and hourly load, a training process is performed to build the adopted model. A real load data set from Tunisian Power Company is used for test, and special attention is paid to the load profile which is specific to warm countries with excessive and unstable demand in summer. The results reflect accuracy and effectiveness of the proposed method, keeping low prediction error for long test periods.