Abstract
Artificial intelligence (AI) has become increasingly popular as a tool to model, identify, optimize, forecast, and control renewable energy systems. This work aimed to evaluate the capability of the artificial neural network (ANN) procedure to model and forecast solar power outputs of photovoltaic power systems (PVPSs) by using meteorological data. For this purpose, based on the literature review, important factors affecting energy generation in a PVPS were selected as inputs, and a recurrent neural network (RNN) architecture was established. After completing the trained network, the RNN capability was assessed to predict the energy output of the PVPS for days not included in the training database. The performance evaluation of the trained RNN revealed a regression value of 0.97774 for test data, whereas the RMSE and the mean actual output power for a sample day were 0.0248 MJ and 0.538 MJ, respectively. In addition to RMSE, an error histogram and regression plots obtained by MATLAB were employed to evaluate the network’s capability, and validation results represented a sufficient prediction accuracy of the trained RNN.
Highlights
Introduction published maps and institutional affilIn the last decades, the energy sector has encountered critical problems, such as growing populations and developing industries, as well as a limited supply of fossil energy sources and market deregulation [1,2]
As a form of artificial intelligence, artificial neural networks (ANNs) are one of the main tools widely used in machine learning
The analysis revealed that the output of the system is mainly dependent on the season; in addition to the parameters previously described, the season must be considered as an input for the neural network
Summary
Introduction published maps and institutional affilIn the last decades, the energy sector has encountered critical problems, such as growing populations and developing industries, as well as a limited supply of fossil energy sources and market deregulation [1,2]. Electric power produced with traditional methods poses serious threats to the global climate and public health [3] To overcome these concerns, the energy network has had to change and improve. Different types of renewable energies such as tidal, wind, solar, wave, geothermal, biomass, and hydropower have been put into operation due to their minimal creation of carbon pollution as well as their ease of access in most places. These sources support about 25% of global electricity generation. Due to the complex relationships between parameters governing renewable energy sources’ integration with the grid, in general, ensuring high energy conversion efficiency and sufficient high-power extract is vital to the successful application of these sources
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