Electric Load Forecasts by Metaheuristic Based Back Propagation Approach

Abstract

The prediction of system load demands a day ahead or a week ahead is called Short Term Load Forecasting. Artificial Neural Network based STLF model has gained significance because of transparency in its modelling, simplicity of execution, and superiority of its performance. The neural model consists of weights whose optimal values are found out by means of different optimization techniques. In this paper, Artificial Neural Network trained by different methods like Back Propagation, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Search model and Bat algorithm is utilized for load forecasting. A thorough analysis of the different techniques is carried out here in order to assess their extent and capability to yield result, by means of dissimilar models, in altered situations. The simulation results indicate that Bat Algorithm based Back Propagation model leads to least forecasting error in comparison to other techniques. However, Cuckoo Search method based Back Propagation model also gives less error relatively, which is very much permissible.

Short-term load forecasting of the distribution system using cuckoo search algorithm

“…The STLF using a feed-forward neural network (FFNN) [20]− [23] is shown in Figure 1. It contains 4 inputs, 25 hidden layers and one output layer with a transfer function.…”