A Statistical Analysis and Artificial Neural Network Behavior on Wind Speed Prediction: Case Study

A Statistical Analysis and Artificial Neural Network Behavior on Wind Speed Prediction: Case Study

The quest for renewable and emission-free energy sources has been encouraged by the increased usage of energy and the decline of fossil fuel supplies, along with an increase in environmental pollution. Wind energy is one of these. In the last few years, the wind power industry has seen exponential growth. The increase in wind turbine orders has resulted in a manufacturer’s market. This disparity in the market, the relative immaturity of the wind industry and the rapid evolution of data processing technologies have provided opportunities to enhance the efficiency of wind farms and to change the myths surrounding their operations. This study provides data-driven modeling, a new paradigm for the wind power industry. For several parameters, each wind mast produces extensive data, recorded as frequently as every minute. Since the predictive performance approach is new to the wind industry, it is important to build a viable road map for study. This paper proposes a long-term wind forecasting (ANN) predictive analysis and data-mining approach, which is ideal for dealing with broad real-world datasets. The paper provides a case study focused on a real database of five years of wind speed data for a location and addresses wind power density results calculated using the probability density functions of Weibull and Rayleigh. Wind speed predicted using wind speed data using intelligent technologies such as Artificial Neural Networks with Datamining methodology (ANN). The MATLAB R2008a Neural Network Toolbox is designed to measure the monthly and annual mean wind speed for the training of the ANN back propagation algorithm and the PROLOG software. The statistical analysis of wind speed prediction shows that the distribution of Weibull is more acceptable than the distribution of Rayleigh and we can infer that higher values of k mean a sharper limit in the frequency distribution curve and thus a lower density of wind power by seeing the values of k.

Aurthor(s) Details:

K. Mahesh
Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology, Bengaluru, India.

View Book :- https://stm.bookpi.org/TPMCS-V6/issue/view/6

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