30-03-2016, 02:35 PM
matlab code forecasting using pso
Abstract
Load forecasting has become a very crucial technique for the efficient functioning of the power system. This paper presents a methodology for the short term load forecasting problem using the similar day concept combined with fuzzy logic approach and particle swarm optimization. To obtain the next-day load forecast, fuzzy logic is used to modify the load curves of the selected similar days of the forecast previous day by generating the correction factors for them. These correction factors are then applied to the similar days of the forecast day. The optimization of the fuzzy parameters is done using the particle swarm optimization technique on the training data set of the considered data set. A new Euclidean norm with weight factors is proposed for the selection of similar days. The proposed methodology is illustrated through the simulation results on a typical data set.
Abstract
Load forecasting has become a very crucial technique for the efficient functioning of the power system. This paper presents a methodology for the short term load forecasting problem using the similar day concept combined with fuzzy logic approach and particle swarm optimization. To obtain the next-day load forecast, fuzzy logic is used to modify the load curves of the selected similar days of the forecast previous day by generating the correction factors for them. These correction factors are then applied to the similar days of the forecast day. The optimization of the fuzzy parameters is done using the particle swarm optimization technique on the training data set of the considered data set. A new Euclidean norm with weight factors is proposed for the selection of similar days. The proposed methodology is illustrated through the simulation results on a typical data set.
