A Meta-Analysis of Solar Forecasting Based on Skill Score

TL;DR

This comprehensive meta-analysis evaluates deterministic solar forecasting methods using skill scores, revealing the impacts of various factors, the superiority of ensemble-hybrid models, and the importance of data types across different forecast horizons.

Contribution

First large-scale meta-analysis of solar forecast skill scores, quantifying impacts of multiple factors and comparing various models with insights on data and horizon-specific performance.

Findings

Forecast horizon dominates impact on skill score.

Ensemble-hybrid models outperform others across horizons.

More training data improves accuracy but risks overfitting.

Abstract

We conduct the first comprehensive meta-analysis of deterministic solar forecasting based on skill score, screening 1,447 papers from Google Scholar and reviewing the full texts of 320 papers for data extraction. A database of 4,687 points was built and analyzed with multivariate adaptive regression spline modelling, partial dependence plots, and linear regression. The marginal impacts on skill score of ten factors were quantified. The analysis shows the non-linearity and complex interaction between variables in the database. Forecast horizon has a central impact and dominates other factors' impacts. Therefore, the analysis of solar forecasts should be done separately for each horizon. Climate zone variables have statistically significant correlation with skill score. Regarding inputs, historical data and spatial temporal information are highly helpful. For intra-day, sky and satellite images show the most importance. For day-ahead, numerical weather predictions and locally measured meteorological data are very efficient. All forecast models were compared. Ensemble-hybrid models achieve the most accurate forecasts for all horizons. Hybrid models show superiority for intra-hour while image-based methods are the most efficient for intra-day forecasts. More training data can enhance skill score. However, over-fitting is observed when there is too much training data (longer than 2000 days). There has been a substantial improvement in solar forecast accuracy, especially in recent years. More improvement is observed for intra-hour and intra-day than day-ahead forecasts. By controlling for the key differences between forecasts, including location variables, our findings can be applied globally.