A Comparative Study on Forecasting Solar Photovoltaic Power Generation Using Artificial Neural Networks

Solar PV power generation is intermittent and results in various grid related issues when integrated to the utility grid. By having prior knowledge of the power generation capabilities of solar photovoltaic (PV) systems, it becomes possible to execute the operation of other power systems in a system...

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Bibliographic Details
Published in2023 Innovations in Power and Advanced Computing Technologies (i-PACT) pp. 1 - 6
Main Authors Salam, Shereen Siddhara Abdul, Petra, M.I., Azad, Abul K., Sulthan, Sheik Mohammed, Raj, Veena
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.12.2023
Subjects
Artificial Neural Network
Artificial neural networks
Forecasting
MATLAB
Photovoltaic systems
Solar irradiance
Solar PV
Systematics
Technological innovation
Weather forecasting
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Summary:Solar PV power generation is intermittent and results in various grid related issues when integrated to the utility grid. By having prior knowledge of the power generation capabilities of solar photovoltaic (PV) systems, it becomes possible to execute the operation of other power systems in a systematic manner, thereby mitigating grid-related issues arising from the intermittent nature of solar PV power generation. This paper discusses PV power forecasting using Artificial Neural Networks by two different approaches. The results obtained from the selected approaches are presented and discussed. The performance evaluation of the selected methods is carried out in terms of root mean square error (RMSE), mean absolute error (MAE) and R 2 values.
DOI:10.1109/i-PACT58649.2023.10434883