Evaluation and Mapping of Wind Energy Potential over Southern Part of India using ANN and GIS Approach

Abstract

Prediction and assessment of wind speed are necessary prerequisites in the sitting and sizing of wind power applications. In this study, an artificial neural network (ANN) model was developed for prediction of wind energy potential in Andhra Pradesh (AP) and Telangana state (TS), India. ANN models are ‘black-box’ modelling technique, with capability to perform nonlinear mapping of a multidimensional input space onto another multidimensional output space without the knowledge of the dynamics of the relationship between the input and output spaces. The geographical parameters (latitude, longitude and altitude) and the month of the year were used as input data, while the monthly mean wind speed was used as the output of the network. Geographical and meteorological data of 30 cities in AP and TS of 20 years (1995–2015) by the India meteorological department, Pune (IMD-Pune) database were used for the training and testing the network. The testing data were not used in the training of the network in order to give an indication of the performance of the system at unknown locations. Statistical error analysis in terms of mean absolute percentage error (MAPE) was conducted for testing data to evaluate the performance of ANN model.