Wind Power Forecasting using Artificial Neural Network

Abstract

The electric energy generated from wind resources is now one of the most important sources in the electrical power system. Predicting wind speed is difficult because wind characteristics are unpredictable, highly variable, and dependent on many factors. This paper presents the design of an artificial neural network used in wind energy forecasting that has been trained using weather data that influences wind energy generation. Artificial Neural Network (ANN) has gained popularity in recent years due to its superior performance. The main objective of the developed model is to improve the forecasting of energy generated from wind farms. The developed system allows the power system operator to determine the best time to rely on the wind farm to produce power for the electrical system without affecting the stability of the system and reducing the cost of electricity generation due to the traditional method. The analysis is performed by investigating wind potential and collecting data from a highly recommended source. The heatmap, covariance and correlation methods are used to analyze the data, and then the data is used to build an Artificial Neural Network (ANN) in MATLAB 2020. The results show very high accuracy 99.9%.