Short time load forecasting is essential for daily planning and operation of electric power system. It is the important
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 forecasting.
However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper
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. Compared
with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance
of the improved model with more precise results and stronger generalization ability is much better than the traditional
methods.
