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Cited by 31Building Energy and Comfort OptimizationBuilding energy efficiency and sustainabilityEnergy Efficiency in Computing

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A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions

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Moraliyage, Harsha, Dahanayake, Sanoshi, De Silva, Daswin, Mills, Nishan, Rathnayaka, Prabod, Nguyen, Su, Alahakoon, Damminda, Jennings, Andrew·2022·Sensors
DOI 10.3390/s22239503PMC PMC9739693
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Abstract

Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions.

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  • Explainable AI has transitioned from a theoretical preference to an operational necessity, essential for ensuring compliance and fostering trust in automated decision-making.

    “Consequently, Explainable AI (XAI) has transitioned from a theoretical preference to an operational necessity, essential for ensuring compliance and fostering trust in automated decision-making [8].”

    From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [8]

    Section 2.4, “Explainability Layer” (page unavailable), calls interpretability a major need for understanding predictive models used in energy measurement and verification. The Conclusions (page unavailable) recommend that energy-market policymakers and regulators consider adopting explainable AI. The paper does not establish a general transition to operational necessity or say that XAI ensures compliance and fosters trust in automated decision-making. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 32

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Metadata

Authors
Moraliyage, H., Dahanayake, S., De Silva, D., Mills, N., Rathnayaka, P., Nguyen, S., Alahakoon, D., & Jennings, A.
Affiliations
  • La Trobe University (AU)
  • Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-12-05
Volume
22
Issue
23
Pages
9503
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22239503
PMC
PMC9739693
OpenAlex
W4311627281
Cited by
31
References
0
Citations by year
2026: 4 · 2025: 12 · 2024: 8 · 2023: 7
FWCI
2.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • La Trobe University (cc-by)
Funders
La Trobe University Net Zero Emissions Program (1)
Topics
  • Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
  • Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
measurement and verification, energy savings estimation, energy-saving technologies, building energy efficiency, explainable AI, predictive modeling, net-zero carbon emissions, building energy consumption, university buildings, greenhouse gas emissions reduction
MeSH terms
Algorithms, Artificial Intelligence, Carbon, Conservation of Energy Resources, Fatigue, Humans, Physical Phenomena

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