An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind-Solar Resource Assessment and Power Estimation

The precise prediction of power estimates of wind-solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input-output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind-solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system's operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg-Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind-solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind-solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.