A generic prediction interval estimation method for quantifying the uncertainties in ultra-short-term building cooling load prediction. (5th June 2020)

• Record Type: Journal Article
• Authors: Zhang, Chaobo; Zhao, Yang; Fan, Cheng; Li, Tingting; Zhang, Xuejun; Li, Junyang
• Is Part Of: Applied thermal engineering. Volume 173(2020)
• Publication Date: 2020-06-05
• Subjects: Cooling load prediction -- Prediction interval estimation -- Artificial neural networks -- Prediction uncertainty -- Data mining; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2020.115261
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A prediction interval estimation method is proposed for cooling loads. A Chebyshev distance-based approach is developed to cluster prediction residuals. The proposed method is statistical assumption-free. The proposed method is generic for various cooling load prediction methods. The proposed method is useful for decision supports of building energy management. Abstract: Ultra-short-term building cooling load prediction is useful for optimal operations of building energy systems. The uncertainties of predicted cooling loads affect the reliability of the optimal operations significantly. This paper proposes a generic prediction interval estimation method to describe the uncertainties quantitatively. The basic idea is that the residual of a predicted cooling load would follow the same distribution as the residuals of historical predicted cooling loads of the similar operating condition. A Chebyshev distance-based agglomerative hierarchical clustering approach is proposed to gather historical prediction residuals of similar operating conditions into the same cluster. A quantile-based approach is proposed to estimate the prediction interval of a predicted cooling load using the cluster of the most similar operating condition. To evaluate the proposed method, an artificial neural network-based cooling load prediction model is trained to predict the cooling loads of a real building. The prediction intervals of the predicted cooling loads are then estimated using the proposed method. The results show that the estimated prediction intervals of the predicted cooling loads are very reliable.