Weather Forecasting Using Photovoltaic System and Neural Network

Abstract

This paper presents the applicability of Artificial Neural Network (ANN) for weather forecasting using a Photovoltaic system. The main objective is to predict daily weather conditions based on various measured parameters gained from the PV system. In this work, Multiple Multilayer Perceptron (MMLP) network with majority voting technique was used and trained using Levenberg Marquardt (LM) algorithm. Voting technique is widely used in many applications to solve real world problem. Different techniques of voting are used such as majority rules, decision making, consensus democracy, consensus government and supermajority. The way of the voting technique is different depending on the problem involved. Majority voting technique was applied in the study so that the performance of MMLP can be approved as compared to single MLP network. The proposed work has been used to classify four weather conditions; rain, cloudy, dry day and storm. The system can be used to represent a warning system for likely adverse conditions. Experimental results demonstrate that the applied technique gives better performance than the conventional ANN concept of choosing an MLP with least number of hidden neurons.