Supplier Short Term Load Forecasting Using Support Vector Regression and Exogenous Input

In power systems, task of load forecasting is important for keeping equilibrium between production and consumption. With liberalization of electricity markets, task of load forecasting changed because each market participant has to forecast their own load. Consumption of end-consumers is stochastic in nature. Due to competition, suppliers are not in a position to transfer their costs to end-consumers; therefore it is essential to keep forecasting error as low as possible. Numerous papers are investigating load forecasting from the perspective of the grid or production planning. We research forecasting models from the perspective of a supplier. In this paper, we investigate different combinations of exogenous input on the simulated supplier loads and show that using points of delivery as a feature for Support Vector Regression leads to lower forecasting error, while adding customer number in different datasets does the opposite.