Intelligent solar photovoltaic power forecasting

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Authors

Poti, Keaobaka D.
Naidoo, Raj
Mbungu, Nsilulu T.
Bansal, Ramesh C.

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

This paper presents a day-ahead forecasting method for photovoltaic (PV) power plants in commercial sectors. The method is based on numerical weather prediction (NWP) models from open weather maps and power plant specifications. The output of the model is the predicted power output from the PV power plant, which is incorporated into an optimal control strategy of the PV plant using battery storage. The use of optimal algorithms assists in the PV power plant curtailment in cases of over-generation and reduces the dependence on conventional power sources such as generators in cases of under-generation by the PV plant. It was found that most forecasting methods do not incorporate PV power and storage systems for proper optimization and demand management. This can be seen as a gap for further research of forecasting models integrated with battery storage systems to improve PV power system performance. Results obtained show a good performance of the developed model. A root means square error (RMSE) of 425.79 W and 595.10 W and a mean absolute error (MAE) of 246.26 W and 238 W were achieved for a summer and winter day, respectively. Furthermore, an excellent positive correlation exists between the predicted output power and the observed results, with R2 values over 90%.

Description

7th International Conference on Renewable Energy and Conservation, ICREC 2022 November 18–20, 2022, Paris, France
DATA AVAILABILITY : Data will be made available on request.

Keywords

Commercial sectors, Demand management, Forecasting, Optimization, PV power plants, System planning, SDG-07: Affordable and clean energy, Photovoltaic (PV), Numerical weather prediction (NWP)

Sustainable Development Goals

SDG-07:Affordable and clean energy

Citation

Poti, K.D., Naidoo, R.M., Mbungu, N.T. et al. 2023, 'Intelligent solar photovoltaic power forecasting', Energy Reports, vol. 9, pp. 343-352. https://DOI.org/10.1016/j.egyr.2023.09.004.