Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

Abstract

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

“…Instead, it learns the relationship from the mean and covariance of the data set and makes predictions using Bayesian inference (Rasmussen & Williams, 2006). GPR has demonstrated exceptional accuracy and robustness in simulating predicted temperatures (Zhang et al., 2021), solar radiation (Lubbe et al., 2020), evaporation (Shabani et al., 2020), and urban environments (Li et al., 2022).…”

Hyper-local temperature prediction using detailed urban climate informatics

“…GPR has demonstrated exceptional accuracy and robustness in simulating predicted temperatures (Zhang et al, 2021), solar radiation (Lubbe et al, 2020), evaporation (Shabani et al, 2020), and urban environments (Li et al, 2022).…”

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

“…This improves the computational capacity of the model to furnish accurate predictions, even under strong irregularities and rapidly changing scenarios. Further, in [73,74], the solar irradiance forecasting is achieved by evaluating the potential of Gaussian process regression. This research opens a new avenue for the development of probabilistic renewable energy management systems to support energy trading platforms and help the smart grid operators with critical decision making during the inherent uncertainty of stochastic power systems.…”