Abstract
Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.
Highlights
Due to the serious problems caused by the use of fossil fuels, much attention has been focused on renewable energy sources (RESs) and smart grid technology to reduce greenhouse gas emissions [1,2]
In [21], the authors constructed two global solar radiation forecasting models based on the artificial neural network (ANN) and random forest (RF) methods
We propose a novel forecasting model for multistep-ahead (MSA) global solar radiation predictions based on the light gradient boosting machine (LightGBM), which is a tree-based ensemble learning technique
Summary
Due to the serious problems caused by the use of fossil fuels, much attention has been focused on renewable energy sources (RESs) and smart grid technology to reduce greenhouse gas emissions [1,2]. Many studies have been conducted to predict global solar radiation accurately based on an ensemble learning technique that combines several weak models. In [21], the authors constructed two global solar radiation forecasting models based on the ANN and random forest (RF) methods They demonstrated that RF, which is an ensemble learning technique, exhibited better prediction performance than the ANN. In [22], the authors proposed four global solar radiation forecasting models based on the bagging and boosting techniques and analyzed the excellence and feature importance of the ensemble learning techniques. We propose a novel forecasting model for multistep-ahead (MSA) global solar radiation predictions based on the light gradient boosting machine (LightGBM), which is a tree-based ensemble learning technique.
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