Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.
Artificial Neural Network and Particle Swarm Optimization for Medium Term Electrical Load Forecasting in a Smart Campus