A hybrid methodology for distribution level photovoltaic power production forecasting verified at the distribution system of Cyprus

Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day-ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud-based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up-scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up-scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up-scaling techniques against the estimated aggregated PV generation demonstrated that the best-performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day-ahead forecasting methodology that is based on an optimal supervised learning and hybrid up-scaling approach.

Automated summary

Accurate PV power forecasting is essential for integrating solar electricity efficiently. This study proposes a novel method for providing day-ahead aggregated PV production forecasts for distributed PV systems. By using clustering and up-scaling, the aggregated PV generation was successfully estimated.