An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

Sami Kabir (), Mohammad Shahadat Hossain and Karl Andersson
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Sami Kabir: Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-931 87 Skellefteå, Sweden
Mohammad Shahadat Hossain: Department of Computer Science & Engineering, University of Chittagong, Chattogram 4331, Bangladesh
Karl Andersson: Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-931 87 Skellefteå, Sweden

Energies, 2024, vol. 17, issue 8, 1-18

Abstract: The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

Keywords: accuracy; building energy; explainability; trust; uncertainties (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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