Multi-Stage Artificial Neural Network Short-term Load Forecasting Engine with Front-End Weather Forecast

A significant portion of electric utility operating expense comes from the energy production. In order to minimize the cost, unit commitment (UC) scheduling is an important tool to properly assign generation units to accommodate the forecasted system demand. The short-term load forecast is a prerequisite for UC planning. The projected load up to 7 days ahead is important for the reconfiguration of generation units. Hour-ahead forecast is used for optimally dispatching online resources to supply the next hour load. This paper addresses the systematic design of artificial neural network based short-term load forecaster (ANNSTLF). The developed ANNSTLF engine has been utilized in a real utility system. The performance analysis over the past year shows that a majority of forecast error was detected in a consistent period with a large temperature forecast error. The enhancement of multi-stage ANNSTLF is proposed to improve the forecasting performance. The comparison of forecasting accuracy due to this enhancement is analyzed