Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons

• Published in: Renewable energy 2020-08, Vol.156, p.279-289
• Main Authors: Pang, Zhihong, Niu, Fuxin, O’Neill, Zheng
• Format: Article
• Language: English
• Subjects: Deep learning; Moving-window; Recurrent neural network; Solar radiation prediction
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2020.04.042

With the rapid advancement of the high-performance computing technology and the increasing availability of the mass-storage memory device, the application of the data-driven models (e.g., the artificial neural network (ANN) model) for solar radiation prediction is appearing in abundance in the past decade. Although the performances of these models have been discussed in a large number of studies, how to further enhance the forecasting accuracies of these data-driven approaches to better facilitate the advanced controls in the building system such as model predictive control (MPC) in smart buildings remains a challenge. Deep learning, which is considered as a powerful tool to move machine learning closer to one of its original goals, i.e., Artificial Intelligence (AI), is a viable solution to this problem. In this study, an ANN model and a recurrent neural network (RNN) model are developed to investigate the performances of the deep learning algorithms for the solar radiation prediction. The actual meteorological data (AMY) from a local weather station in Alabama is used for the training process. Various scenarios, including different sampling frequencies and moving window algorithms, are included for a comprehensive evaluation of the accuracies and efficiencies. The results suggest that compared with the ANN model, the solar radiation prediction using the RNN model has a higher prediction accuracy, with a 47% improvement in Normalized Mean Bias Error (NMBE) and a 26% improvement in Root-Mean-Squared Error (RMSE). Besides, this forecasting accuracy could even be taken to a higher level by increasing the granularity of the data or adding a moving-window algorithm to the prediction model. By increasing the sampling frequency of the training data from 1 h to 10 min, the Root-Coefficient of Variation Mean-Squared Error (CV(RMSE)) of the ANN model dropped from 30.9% to 9.41%, while the CV(RMSE) of the RNN model dropped from 9.83% to 7.64%. For the RNN model, the NMBE was improved from 0.9% to 0.2% after the implementation of the moving-window algorithm. Besides, it was found that the cloud cover could have a significant impact on the prediction accuracy. •Development of a RNN based deep learning algorithm for solar radiation prediction.•Comparisons of RNN with traditional ANN for solar radiation prediction.•The prediction performance with the RNN is moderately improved with additional computational cost.•Improved prediction performance through a moving window al