A machine learning-based framework for cost-optimal building retrofit

Chirag Deb, Zhonghao Dai and Arno Schlueter

Applied Energy, 2021, vol. 294, issue C, No S030626192100458X

Abstract: The current process of analysing building retrofit strategies relies on physics-based, thermal balance models. However, these models are oblivious to the significance of the input variables for devising the retrofit strategies. This leads to the process of exhaustive search for obtaining the cost-optimal retrofit strategy. On the contrary, this work presents a framework for a data-driven, cost-optimal retrofit analysis based on machine learning (ML) techniques which capitalizes on the importance of the input variables. The framework involves four steps, which are feature selection, model development, feature significance and cost-optimal retrofit analysis.

Keywords: Building energy model; Cost-optimal retrofit; Machine learning; Feature significance; Recurrent neural network (RNN); Wireless sensor network (WSN) (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.apenergy.2021.116990

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