In this paper, a multi-variable echo state network trained with Bayesian regulation has been developed for the short-time load forecasting. In this study, we focus on the generalization of a new recurrent network. Therefore, Bayesian regulation and Levenberg-Marquardt algorithm is adopted to modify the output weight. The model is verified by data from a local power company in south China and its performance is rather satisfactory. Besides, traditional methods are also used for the same task as comparison. The simulation results lead to the conclusion that the proposed scheme is feasible and has great robustness and satisfactory capacity of generalization.

No abstract

No abstract

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.