Impact of Forecast Time-Step on PV Production Accuracy Using Machine Learning for Micro-Grid Efficiency - P.R. Sudha Rani; S. Ajit; M. Shunmugasundaram; S. Gangadharan; Swetha Mareddy; R. Senthamil Selvan; M. Prabha (Metallurgical and Materials Engineering, 2025)
Efficient energy management solutions are becoming more important with emergence of micro-grids that include photovoltaic generation & storage. Their prediction of energy output over the near to long term is an important part of their work. An essential parameter influencing the forecast’s accuracy, optimum control time discretisation, efficiency, and computing load is the forecast time-step. This trade-off is measured by putting four machine learning (ML) forecast methods through their paces on two different sites, with time-steps ranging from 2 to 60 minutes as well as horizons from 10 minutes to 6 hours. The methods are evaluated on both horizontal and tilted global irradiance charts, depending on the availability of data. All of the methods show comparable findings, which show that for predictions less than an hour and between one and six hours, the error measure may be decreased by up to 1.9% every minute on the time-step, and by up to 2.8% every ten minutes. Additionally, it is demonstrated that for short-term horizons, it could be beneficial towards make high-resolution forecasts & then average results at time-step required by energy control scheme.