Medium-Term Load Forecasting Based on Genetic Algorithm Optimised Neural Network
Medium-term power load forecasting is extremely important for grid planning and generation planning as well as power market operation.For the medium-term power load forecasting in a certain place,BP neural network is widely used in load forecasting because of its powerful nonlinear mapping ability.However,the BP algorithm itself has some defects,such as easy to fall into local minima and slower convergence speed.The genetic algorithm with its excellent global search ability can well solve this problem,using the global optimisation ability of GA to systematically adjust the initial connection weights and bias values of the BP network,which significantly reduces the possibility of the network falling into the suboptimal solution during the learning process.The convergence speed is improved.The results of the study show that the integrated model has significant improvement in both prediction accuracy and stability compared to the single BP neural network and other comparative models.