An expert system approach to short-term load forecasting for Reliance Energy Limited, Mumbai

Abstract

Economically efficient generation scheduling requires accurate forecasting of load. In this paper, we propose a short term load forecasting program for Reliance Energy Limited (REL) in Mumbai region. The method is based on a similar day approach. The development of forecast engine involves 4-steps. The first step involves discussion with domain experts (utility engineers) to extract and learn the rules regarding system behaviour. In the next step, these rules are refined by statistical analysis. A linear prediction model for each day of week is developed. The third step involves an adaptive implementation of the rules. The parameters of the linear model are learned from previous data by solving an optimization problem. Quadratic programming is used with redundancy factor 2. The final step involves fine-tuning of forecast by re-shaping the forecast as the reference day using fast Fourier transform, filtering and smoothening by 3-point moving average technique. Normalization is done using DC component of reference day. Since the parameters are learnt from past few weeks data, the seasonal variations due to change in season like winter, summer are better modeled. Detailed study of the results of the forecast program, the overall mean absolute percentage error (MAPE) of the forecasted data is 2.89 over an interval from Aug'04 to May'05 which is reasonable