A Study of Four Types of Neural Networks with an Error Correction Approach Applied to Predict Wind Speed

Abstract

AbstractNowadays, renewable energy resources receive attention to meet the Kyoto Protocol and are environmentally friendly. Wind farm and power system planning, maintenance, and control obligate the prediction of wind speeds. Based on four inputs such as wind speed, temperature, relative humidity, and wind direction, this paper studies a set of artificial neural networks that include the Elman Network (EN), Multilayer Perceptron Network (MLPN), Improved Back Propagation Network (IBPN), and Recursive Radial Basis Function Network (RRBFN) associated with the Error Correction (EC) approach based prediction of wind speed. We perform various hidden neuron-based analyses regarding the wind speed prediction using the proposed four inputs based on a different artificial neural network with an error correction approach. The presented prediction model experimental outcome-based study confirms that compared to the EN-EC, MLPN-EC, and IBPN-EC-based prediction models, the minimal performance metrics achieved concerning the proposed four inputs associated prediction model using Recursive Radial Basis Function Network (RRBFN) with an error correction approach.KeywordsElman NetworkImproved Back-Propagation NetworkMultilayer Perceptron NetworkRecursive Radial Basis Function NetworkWind Speed Prediction