Research On Power Short-Term Load Forecasting Method Based On Intelligent Method

Power short-term load forecasting (STLF) is an important routine for system dispatch whose accuracy directly influences the security, economy and supply quality of power system. This problem has attracted much attention and remains an academic spot since the electric load is essentially non-determinate, non-linear and stochastic. With the development of power market, load behaviors more complicated which highlights the discrepancy between forecasting complexity and solutions; previous single forecasting methods cannot obtain satisfied results. Therefore, intelligent and integrated approaches become the current mainstream of STLF.This thesis analyzes the theory and methods of electric load forecasting in a deep manner and provides scientific decision-making for system management departments. Forecasting day is first classified as work day and holiday according to load change patterns; then intelligent optimized methods and integrated forecasting strategies are introduced considering various factors such as meteorological condition and spot price. Main work and creative results are as follows: 1) Factors that influence load forecasting and reasons of forecasting are analyzed. Aiming at abnormal data in historical load series, a kernel based fuzzy-C mean clustering method (WKFCM) is then proposed. The WKFCM measures distance by kernel functions instead of the complicated Euclidean distance and this kernel based distance is used as dissimilarity function of target clustering formula which can reduce the calculation complexity. After the clustering, a super circle neural network based identification model for load data is proposed. This CC model classifies the sample space in a nonlinear manner and can fulfill optimal or sub-optimal classification; the network structure is easy to understand and to train, and has a strong ability of error tolerance even with few hidden neurons. .(2) Optimized clone immune and controllable excitation function are combined to establish a BPNN forecasting model for work day forecasting. The excitation function controls the BP algorithm which greatly accelerates convergence of BP training; the self adaptable strategy based clone immune optimizes the controlled BP algorithm; it improves its global searching ability better than the BP algorithm optimized by chaos and avoids the algorithm to be trapped in local minimum.(3) Besides the work days, holidays are classified in weekends and major holidays. For the weekend forecasting, an immune particle swam optimization based least support vector machine (IPSO-LS-SVM) is proposed. The antibody diversity mechanism of immune system is introduced into PSO which improve the convergence performance since the particle diversity is kept without compromising the existence of high-adaptable particles. Major holidays such as the New year, Spring festival, May Day and National Day usually have a long forecasting span and lack of reference load data; meanwhile load in these days are more prone to be influenced by meteorological condition. Considering these features, the GM (1, 1) model is combined with the Markov chain to forecast load in major holidays. The GM (1, 1) gives raw forecasting results, and these results are then refined by the the Markov chain taking temperature's influence on holiday load and similarity of the weekend data into account. The proposed method reduces large forecasting error given by the traditional grey system and thus improves forecasting accuracy further.(4) Based on correlation analysis of spot power price and STLF, a STLF model using PSO based general regression neural network (GRNN-PSO) and ANFIS is proposed. This model makes good use of the self-learning and non-linear mapping abilities of neural network. First, a brief introduction of GRNN-PSO is presented, then ANFIS is used to rectifies the outputs of GRNN-PSO.. This method effectively overcomes the shortcoming of NN-based forecasting in power market and objectively reflects the relationship between load and price; as a consequence, the accuracy of load forecasting under power market is improved.?...