Fuzzy modeling and similarity based short term load forecasting using evolutionary particle swarm optimization

There are a lot of uncertainties in planning and operation of electric power system, which is a complex, nonlinear, and non-stationary system. Advanced computational methods are required for planning and optimization, fast control, processing of field data, and coordination across the power system for it to achieve the goal to operate as an intelligent smart power grid and maintain its operation under steady state condition without significant deviations. One of the important aspects to operate power system in such manner is accurate and consistent short term load forecasting (STLF). This paper presents a methodology for the STLF using the similar day concept combined with fuzzy logic approach and evolutionary particle swarm optimization (EPSO) technique. A Euclidean distance norm with weight factors considering the weather variables and day type is used for finding the similar days. Fuzzy logic is used to modify the load curves of the selected similar days of the forecast by generating the correction factors for them. The input parameters for the fuzzy system are the average load, average temperature and average humidity differences of the forecasted previous days and their similar days. These correction factors are applied to the similar days of the forecast day. The tuning of the fuzzy input parameters is done using the EPSO technique on the training data set of the considered data and tested. The results of load forecasting shows that the proposed EPSO tunes fuzzy system provides better results than the fuzzy stand alone system (without EPSO).