ElectricLoadCombinedForecastingModelWeights.doc

Electric Load Combined Forecasting Model Weights Optimization Using An Improved Particle Swarm Algorithm  MACROBUTTON MTEditEquationSection2 Equation Chapter 1 Section 1 SEQ MTEqn \r \h \* MERGEFORMAT  SEQ MTSec \r 1 \h \* MERGEFORMAT  SEQ MTChap \r 1 \h \* MERGEFORMAT  Jiang Chuanwen*, Ma Yuchao*, Liu Yong* * Department of Electrical Engineering, Shanghai Jiaotong University Huashan Road 1954, Shanghai, P.R China 200030 Lu Jianyu**, Wang Liang** ** East China Grid Company Limited. Shanghai, P.R China ABSTRACT：The Electric load series always presents complex phenomenon because of the influence of many complicated facts, various forecasting results can be obtained by using different models for a given electric power utility. The combined forecasting model is recognized as an appreciative method. The paper introduces an improved Particle swarm optimization (PSO) for electric load combination forecasting model weight optimization. The new method applies a self-adaptive weight scale operator to avoid being trapped in the local optimum in conventional Particle swarm optimization. The proposed method has been examined and tested on a practical system. The test results show that the improved PSO has better convergence and faster calculation speed than the basic PSO, and the presented combination forecast model has improved the accuracy. KEY WORDS：Load forecasting, Combination forecasting model, Chaos, Particle Swarm Optimization Introduction The forecasting of electric load has always been important for the secure and economically beneficial operation of a power system. The short-term load forecasting has attracted many scholars’ interests in the modeling theory of forecasting for a long time. Some effective achievements have been harvested [1]-[5]. But The electric load series always presents complex phenomenon because of the influence of many complicated facts, various forecasting results can be obtained by using different models for a given el