Solar thermal generation forecast via deep learning and application to buildings cooling system control. (August 2022)

• Record Type: Journal Article
• Authors: Rana, Mashud; Sethuvenkatraman, Subbu; Heidari, Rahmat; Hands, Stuart
• Is Part Of: Renewable energy. Volume 196(2022)
• Page Start: 694
• Page End: 706
• Publication Date: 2022-08
• Subjects: Solar thermal power -- Solar cooling -- Convolutional neural networks -- Deep learning -- Time series prediction -- Multivariate models; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.07.005
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Reliable prediction of solar thermal power is essential for optimal operation and control of renewable energy driven distributed power systems. This paper presents a Convolutional Neural Networks (CNNs) based multivariate approach for forecasting power generation from solar thermal collectors over multiple horizons simultaneously. It also demonstrates an application of solar thermal power generation forecasting in a building cooling system as part of a predictive central controller. Historical data from an evacuated collector field and a single axis tracking collector field have been used to develop the prediction models and assess the performance of the proposed approach. Experimental results show that the proposed approach provides accurate prediction for multiple forecast horizons: MAPE is 2.99%–4.18% for 30 min to 24 h ahead prediction. The proposed approach utilising both historical and predicted future weather data achieves 25%–37% improvements of accuracy compared to its univariate counterpart that uses only lagged power data as input. It also outperforms existing data driven approaches based on NNs, LSTM, and RF, and achieves 5.46%–21.28% statistically significant improvements compared to them. Highlights : ● Application of Convolutional Neural Networks to forecast solar thermal power output. ● Data cleaning based on an unsupervised ML method (called local outlier factor). ● Input variables selection by applying mutual information. ● Evaluation of prediction model using data for two types of solar thermal collectors. ● 5–21% improvement of prediction accuracy over existing data-driven approaches.