Exploring Key Weather Factors From Analytical Modeling Toward Improved Solar Power Forecasting

Abstract

Accurate solar power forecasting plays a critical role in ensuring the reliable and economic operation of power grids. Most of existing literature directly uses available weather conditions as input features, which might ignore some key weather factors and the coupling among weather conditions. Therefore, a novel solar power forecasting approach is proposed in this paper by exploring key weather factors from photovoltaic (PV) analytical modeling. The proposed approach is composed of three engines: (1) analytical modeling of PV systems; (2) machine learning methods for mapping weather features with solar power; and (3) a deviation analysis for solar power forecast adjustment. In contrast to the existing research that directly uses available weather conditions, this paper explores the physical knowledge from PV models. Different irradiance components and PV cell temperatures are derived from PV analytical modeling. These weather features are used to reformulate the input of machine learning methods, which helps achieve a better forecasting performance. Moreover, based on the historical forecasting deviations, a compensation term is presented to adjust the solar power forecast. Case studies based on measured datasets from PV systems in Australia demonstrate that the forecasting performance can be highly improved by taking advantage of the key weather features derived from PV models.