A Comprehensive Review of Artificial Neural Network Techniques for Predicting Photovoltaic Output

Main Article Content

Nur Ellysa Mu’izz Mohd Zaini
Ahmad Fateh Mohamad Nor

Abstract

Photovoltaic (PV) systems have gained popularity due to their ability to harness renewable solar energy,
contributing to environmental sustainability and reducing dependence on fossil fuels. Accurately predicting PV power output is crucial as it enables efficient energy management to ensure a reliable and consistent energy supply from these renewable sources. This is where advanced methods like Artificial Neural Network (ANN) become important, as they can effectively handle the complexities and variabilities associated with solar energy generation. This paper reviews ANN techniques for predicting photovoltaic (PV) system output. It begins by highlighting the increasing significance of PV systems in renewable energy and the essential need for precise output prediction to optimize energy utilization. The review then focuses on ANNs as a preferred method for prediction, due to their ability to process complex, nonlinear data inherent in solar energy generation, offering a comprehensive analysis of various ANN configurations and their efficacy in different scenarios. The ANN techniques for predicting PV output, encompassing various aspects such as ANN configurations, neuron counts, and training methods are also included. It delves into the nuances of data collection in different geographical areas and the impact on ANN performance. The findings highlight the potential of optimized ANN settings to enhance the accuracy of renewable energy forecasting, suggesting future research directions in this domain.

Article Details

How to Cite
[1]
Nur Ellysa Mu’izz Mohd Zaini and Ahmad Fateh Mohamad Nor, “A Comprehensive Review of Artificial Neural Network Techniques for Predicting Photovoltaic Output”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 1, pp. 37–42, Jan. 2024.
Section
Reviews

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