Citation: Moraliyage, H.;
Dahanayake, S.; De Silva, D.; Mills,
N.; Rathnayaka, P.; Nguyen, S.;
Alahakoon, D.; Jennings, A. A Robust
Artificial Intelligence Approach with
Explainability for Measurement and
Verification of Energy Efficient
Infrastructure for Net Zero Carbon
Emissions. Sensors 2022, 22, 9503.
https://doi.org/10.3390/s22239503
Academic Editor: Hossam A. Gabbar
Received: 19 October 2022
Accepted: 1 December 2022
Published: 5 December 2022
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Article
A Robust Artificial Intelligence Approach with Explainability
for Measurement and Verification of Energy Efficient
Infrastructure for Net Zero Carbon Emissions
Harsha Moraliyage, Sanoshi Dahanayake, Daswin De Silva * , Nishan Mills , Prabod Rathnayaka ,
Su Nguyen, Damminda Alahakoon and Andrew Jennings
Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia
* Correspondence: d.desilva@latrobe.edu.au
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.
Keywords:
artificial intelligence; Energy Conservation Measures (ECM); Measurement and Verifica-
tion (M&V); explainability AI (XAI); energy efficiency; baseline modeling
1. Introduction
A global transition into net zero carbon emissions has been accepted, and in some
cases mandated, by governments, organizations, and concerned communities as the critical
and pragmatic solution to climate change. An increase in renewables generation and a
decrease in energy consumption delivers climate action, as well as energy sustainability
and reduced operational costs. Governmental policies on energy efficiency are aimed at
supporting climate action, such as the Energy Policy Act (EPA), an executive order passed
by the US government to improve the energy efficiency in 15% of the buildings by 2015
with respect to the 2003 baseline and the US Climate Bill 2022 proposed investment worth
nearly $370 billion towards energy efficiency and climate change reduction efforts [
1
,
2
].
The Energy Efficiency Directive by the European Parliament has the objective of reducing
greenhouse gas emissions by 55% compared to 1990 levels to achieve climate neutrality in
2050 and improve energy efficiency across all industries by 9% compared to 2020 levels [
3
].
Sensors 2022, 22, 9503. https://doi.org/10.3390/s22239503 https://www.mdpi.com/journal/sensors
