2023
DOI: 10.1016/j.enbuild.2023.113195
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Blending of energy benchmarks models for residential buildings

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Cited by 10 publications
(2 citation statements)
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“…This approach discloses opportunities to order energy-saving processes, such as modifications to end-use appliances or building operations. The proposed approach employs machine-learning techniques to maximize accuracy and precision compared to other benchmarking methods (Gupta et al 2023a, Konhauser et al 2022Gupta et al 2022). Data gathering and availability of relevant information to process simulation models and use tools and techniques to evaluate building performance is crucial.…”
Section: Buildings and Energymentioning
confidence: 99%
“…This approach discloses opportunities to order energy-saving processes, such as modifications to end-use appliances or building operations. The proposed approach employs machine-learning techniques to maximize accuracy and precision compared to other benchmarking methods (Gupta et al 2023a, Konhauser et al 2022Gupta et al 2022). Data gathering and availability of relevant information to process simulation models and use tools and techniques to evaluate building performance is crucial.…”
Section: Buildings and Energymentioning
confidence: 99%
“…This approach discloses opportunities to order energy-saving processes, such as modifications to end-use appliances or building operations. The proposed approach employs machine-learning techniques to maximize accuracy and precision compared to other benchmarking methods [44][45][46]. Data gathering and availability of relevant information to process simulation models and use tools and techniques to evaluate building performance is crucial.…”
Section: Buildings and Energymentioning
confidence: 99%