Solar PV power forecasting at Yarmouk University using machine learning techniques

Abstract Renewable energy sources are considered ubiquitous and drive the energy revolution. Energy producers suffer from inconsistent electricity generation. They often struggled with the unpredictability of the weather. Thus, making it challenging to balance supply and demand. Technologies like artificial intelligence (AI) and machine learning are effective ways to forecast, distribute, and manage renewable photovoltaic (PV) solar supplies. AI will make the energy forecasting system more connected, intelligent, reliable, and sustainable. AI can innovate how energy is used and help find solutions for decarbonizing energy systems. There are potential advantages to total energy forecasting. AI can support the growth and integration of PV solar energy. The article’s main objective is to use AI to forecast the output consumed power of the Yarmouk University PV solar system in Jordan. The total actual yield is 5548.96 MW h, and the performance ratio (PR) is 95.73%. Many techniques are used to predict the consumed solar power. The random forest model obtains the best results of root mean squared error and mean absolute error are 172.07 and 68.7, respectively. This accurate prediction allows for the maximum use of solar power and the minimal use of grid power. This work guides the operators to learn trends embedded in Yarmouk University’s historical data. These understood trends can be used to predict the consumption of solar power output. Thus, the control system and grid operators have advanced knowledge of the expected consumption of solar power at each hour of the day.