It is of great significance to achieve the prediction of building energy consumption. However, machine learning, as a promising technique for many practical applications, was rarely utilized in this field. The most important reason is that the predictive structure with best performance is difficult to be determined. To fill the gap, this paper offers one in-depth review, which focuses on the accuracy analyses and model comparisons. Specifically, the accuracy analyses were conducted based on different types of buildings (e.g. residential building, commercial building, government building or educational building), different type of temporal granularity (e.g. sub-hourly, hourly, daily or annual), as well as input/output variables and historical data collections. Further, artificial neural network (ANN) and support vector machine (SVM), as the epidemic models, were compared in terms of their complexity of prediction processes, accuracies of results, the amounts of required historical data, the numbers of inputs, etc. Then the hybrid and single machine learning methods were outlined and compared in terms of their strengths and weaknesses. In addition, several vital defects and further research directions are presented from a multivariate perspective. We hope
The ferromagnetic ordering temperature of SrRu 1−x Cr x O 3 increases to 175 and 186 K for x = 0.05 and 0.12, respectively, from 162 K for SrRuO 3 (x = 0). 53 Cr and 99,101 Ru nuclear magnetic resonance reveals that Cr is in a 'valence state' of Cr 3+ (t 3↑ 2g ), and Ru is in a mixed valence state, namely, Ru 4+ (t 3↑1↓ 2g ) and Ru 5+ (t 3↑ 2g). A single Ru NMR signal corresponding to Ru (4+δ)+ is observed, indicating that the spin-down electron in the Ru 4d shell is less localized in SrRu 1−x Cr x O 3 (x = 0). This result is consistent with a broadened Ru t 2g band and a possible Ru 4+ (d 4 )-O 2− -Ru 5+ (d 3 ) as well as Ru 4+ (d 4 )-O 2− -Cr 3+ (d 3 ) double-exchange interaction. This exchange interaction involves the Cr 3+ in the ferromagnetic ordering and enhances the ordering temperature.
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