A Comparative Study on Forecasting Solar Photovoltaic Power Generation Using Artificial Neural Networks
- Resource Type
- Conference
- Authors
- Salam, Shereen Siddhara Abdul; Petra, M.I.; Azad, Abul K.; Sulthan, Sheik Mohammed; Raj, Veena
- Source
- 2023 Innovations in Power and Advanced Computing Technologies (i-PACT) Innovations in Power and Advanced Computing Technologies (i-PACT), 2023. :1-6 Dec, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Photovoltaic systems
Solar irradiance
Technological innovation
Systematics
Weather forecasting
Artificial neural networks
Forecasting
Solar PV
forecasting
Artificial Neural Network
MATLAB
- Language
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.