Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis
نویسندگان
چکیده
Prediction of building energy consumption plays an important role in conservation, management, and planning. Continuously improving enhancing the performance forecasting models is key to ensuring sustainability systems. In this connection, current paper presented a new improved hybrid model machine learning application for cooling load (CL) heating (HL) residential buildings after studying analyzing various types CL HL models. The proposed model, called group support vector regression (GSVR), combination method data handling (GMDH) (SVR) To forecast HL, study also made use base methods such as back-propagation neural network (BPNN), elastic-net (ENR), general (GRNN), k-nearest neighbors (kNN), partial least squares (PLSR), GMDH, SVR. technical parameters were utilized input variables models, adopted output each network. All saved form black box training initial testing. Finally, comparative analysis was performed assess predictive suggested well-known basic Based on results, with high correlation coefficient (R) (e.g. R=99.92% R=99.99% forecasting) minimal statistical error values provided most optimal prediction performance.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3136091