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Indexed by:Journal Papers

Date of Publication:2019-10-01

Journal:SUSTAINABLE CITIES AND SOCIETY

Included Journals:EI、SCIE

Volume:50

ISSN No.:2210-6707

Key Words:Rural residences; Inner Mongolia; Energy consumption; Space heating; Feature selection; Support vector machine (SVM) model; Sensitivity analysis; Partial least squares regression(PLSR); Random forest(RF)

Abstract:Identification of optimal subsets of input variables is a primary task in data-driven prediction modeling for the coal consumption for space heating (CCSH) of rural residences. However, most related predictive models of CCSH in rural residences ignore the nonlinear relationship between the factors and CCSH, and are short of feature selection process. This paper proposed an enhanced CCSH prediction model with learning-based optimized feature selection based on the measured weekly CCSH during real operation in Chifeng, Inner Mongolia, China. Partial least squares regression and random forest were employed to rank the features, and ten models with various input subsets were established by support vector regression. The prediction accuracy of the ten models was compared and the optimal features were examined based on the coefficient of variation of the root mean square error (CVRMSE), coefficient of determination (R-2) and model generalization ability. Furthermore, the residual errors between predicted and measured CCSH are distributed around zero evenly and extracted from the normal distribution for the optimized model. Particularly, we employed the best model to predict the aggregate CCSH at the district level. The prediction model with the optimal inputs was verified to be reasonable and accurate at the individual and district scales.