Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

Short time load forecasting is essential for daily planning and operation of electric power system. It is the im- portant basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series fore- casting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This pa- per will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Com- pared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the per- formance of the improved model with more precise results and stronger generalization ability is much better than the tra- ditional methods.