Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning

Abstract

Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting, where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production. Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather, and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions. However, ensemble prediction systems are known to exhibit systematic errors, and thus require post-processing to obtain accurate and reliable probabilistic forecasts. The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy: not applying any post-processing at all; post-processing only the irradiance predictions before the conversion; post-processing only the solar power predictions obtained from the model chain; or applying post-processing in both steps. In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S., we develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance (GHI) and solar power generation. Further, we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain. Our results indicate that postprocessing substantially improves the solar power generation forecasts, in particular when post-processing is applied to the power predictions. The machine learning methods for post-processing slightly outperform the statistical methods, and the direct forecasting approach performs comparably to the post-processing strategies.

摘要

数值天气预报模型的天气预测结果在太阳能预测中起着核心作用, 进一步基于物理模型链方法将太阳辐照度预测值转为光功率预测值. 天气模型的集合预报主要用于量化未来天气预报的不确定性, 并可以将不确定性通过模型链传播, 生成概率性的太阳能预测. 然而, 集合预报系统通常会产生系统性误差, 因此需要通过后处理以获得准确可靠的概率预测. 本研究目标是针对太阳能预测, 系统评估不同后处理方法效果, 包括不进行任何后处理; 仅在辐照度预测进行后处理; 仅对通过模型链后的光功率预测进行后处理; 或对辐照度预测和光功率预测值均进行后处理. 基于美国Jacumba太阳能电站的基准数据集, 本文发展了针对总辐照度和光功率的集合预测的统计和机器学习方法. 此外, 我们提出了一种基于神经网络的光功率预测模型, 替代模型链. 结果表明, 后处理方法显著提高了太阳能预测的准确性, 尤其是在对光功率预测值进行后处理. 基于机器学习的后处理方法略微优于统计方法, 直接预测方法与最佳后处理策略的表现相当.