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This work was supported by National Natural Science Foundation of China (Grant Nos. 61873114, 51705206), China Postdoctoral Science Foundation (Grant Nos. 2018T110457, 2016M601741), and Project Foundation for Priority Academic Program Development of Jiangsu Higher Education Institutions.
Appendixes A–D.
[1] Wang Z, Srinivasan R S. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews, 2016, 75: 796--808 doi:10.1109/WSC.2015.7408504. Google Scholar
[2] Li K, Hu C, Liu G. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Buildings, 2015, 108: 106-113 CrossRef Google Scholar
[3] Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput-Aided Des, 2011, 43: 303-315 CrossRef Google Scholar
[4] Chen X, Yu K, Du W. Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy, 2016, 99: 170-180 CrossRef Google Scholar
[5] Pang B, Liu M, Zhang X. A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts. Sci China Inf Sci, 2018, 61: 022312 CrossRef Google Scholar
Figure 1
(Color online) (a) Flowchart for predicting city-level energy consumption and (b) predicted daily city electrical loads using iTLBO-ANN model with/without data pre-processing.
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