A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants

Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a combined method for day-ahead solar power generation (SPG) prediction of multi-region photovoltaic (PV) plants, which integrates automatic machine learning, improved genetic algorithm, and SPG physical model. The method is evaluated with real data from multi-region PV plants in Hokkaido, demonstrating acceptable accuracy and outperforming several baselines and other methods.