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.
