Fu Xiao

TL;DR: Wang et al. as mentioned in this paper investigated the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles.

TL;DR: This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy.

TL;DR: In this article, the authors present an overall review on the state of the art of the research and applications of quantitative energy performance assessment, and a framework is proposed for categorizing the energy quantification methods and performance benchmarking methods for energy performance assessing for existing buildings, i.e., the calculation-based, measurement-based and hybrid methods, according to the energy data acquisition approaches.

TL;DR: In this article, a comprehensive review of peak load shifting control strategies using thermal energy storage facilities in commercial buildings is presented and discussed, and further efforts needed for developing more applicable load shifting management strategies using the facilities are also addressed.

TL;DR: This study shows that DM techniques are valuable for knowledge discovery in BAS database; however, solid domain knowledge is still needed to apply the knowledge discovered to achieve better building operational performance.