Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites

Due to the growing penetration of behind-the-meter (BTM) photovoltaic (PV) installations, accurate solar energy forecasts are required for a reliable economic energy system operation. A new hybrid methodology is proposed in this paper with a sequence of one-step ahead models to accumulate 144 h for a small-scale BTM PV site. Three groups of models with different inputs are developed to cover 6 days of forecasting horizon, with each group trained for each hour of the above zero irradiance. In addition, a novel dataset preselection is proposed, and neighboring solar farms' power predictions are used as a feature to boost the accuracy of the model. Two techniques are selected: XGBoost and CatBoost. An extensive assessment for 1 year is conducted to evaluate the proposed method. Numerical results highlight that training the models with the previous, current, and 1 month ahead from the previous year referenced by the target month can improve the model's accuracy. Finally, when solar energy predictions from neighboring solar farms are incorporated, this further increases the overall forecast accuracy. The proposed method is compared with the complete-history persistence ensemble (CH-PeEn) model as a benchmark.

Automated summary

In this paper, a new hybrid methodology is proposed to provide accurate solar energy forecasts for small-scale BTM PV sites. The method utilizes XGBoost and CatBoost techniques and incorporates neighboring solar farms' power predictions as a feature to improve model accuracy. Numerical results show that training the models using data from the previous, current, and future months can enhance accuracy. Finally, incorporating solar energy predictions from neighboring solar farms further increases forecast accuracy.