This research focuses on preference learning and feature validity. Sixteen machine learning and regression methods are evaluated to realize this problem.
Having a reliable approximation of heating load (HL) and cooling load (CL) is a substantial task for evaluating the energy performance of buildings (EPB).
Learning. Article. Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms.
Detiles of Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms By ...
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Feb 29, 2024 · Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms.
Thus, the main effort of this study was to evaluate the capability of several learning methods for appraising the HL and CL of a residential building. To this ...
Sep 8, 2023 · ... Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms.
Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Article. Sep ...
Mar 23, 2022 · Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms.