Benchmarks for Solar Radiation Time Series Forecasting

TL;DR

This paper evaluates naive and ensemble forecasting methods for solar radiation time series, proposing a formal benchmarking framework and identifying the most effective approaches for different conditions.

Contribution

It introduces a rigorous benchmarking formalism for solar radiation forecasting, compares multiple naive and ensemble methods, and highlights the effectiveness of the ARTU model and model combinations.

Findings

ARTU and ensemble methods outperform other naive models.

Benchmarking results vary depending on forecast horizon and data characteristics.

Proper selection of naive reference models is crucial for fair evaluation.

Abstract

With an ever-increasing share of intermittent renewable energy in the world's energy mix,there is an increasing need for advanced solar power forecasting models to optimize the operation and control of solar power plants. In order to justify the need for more elaborate forecast modeling, one must compare the performance of advanced models with naive reference methods. On this point, a rigorous formalism using statistical tools, variational calculation and quantification of noise in the measurement is studied and five naive reference forecasting methods are considered, among which there is a newly proposed approach called ARTU (a particular autoregressive model of order two). These methods do not require any training phase nor demand any (or almost no) historical data. Additionally, motivated by the well-known benefits of ensemble forecasting, a combination of these models is considered, and then validated using data from multiple sites with diverse climatological characteristics, based on various error metrics, among which some are rarely used in the field of solar energy. The most appropriate benchmarking method depends on the salient features of the variable being forecast (e.g., seasonality, cyclicity, or conditional heteoroscedasity) as well as the forecast horizon. Hence, to ensure a fair benchmarking, forecasters should endeavor to discover the most appropriate naive reference method for their setup by testing all available options. Among the methods proposed in this paper, the combination and ARTU statistically offer the best results for the proposed study conditions.