{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:54:03Z","timestamp":1774648443723,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"La Trobe University Net Zero Emissions Program","award":["1"],"award-info":[{"award-number":["1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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&amp;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&amp;V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&amp;V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&amp;V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&amp;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&amp;V for energy-efficient building infrastructure and net zero carbon emissions.<\/jats:p>","DOI":"10.3390\/s22239503","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T08:10:57Z","timestamp":1670227857000},"page":"9503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6212-8312","authenticated-orcid":false,"given":"Harsha","family":"Moraliyage","sequence":"first","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"given":"Sanoshi","family":"Dahanayake","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-5969","authenticated-orcid":false,"given":"Daswin","family":"De Silva","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-3767","authenticated-orcid":false,"given":"Nishan","family":"Mills","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2078-057X","authenticated-orcid":false,"given":"Prabod","family":"Rathnayaka","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"given":"Su","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"given":"Damminda","family":"Alahakoon","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5720-2736","authenticated-orcid":false,"given":"Andrew","family":"Jennings","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.apenergy.2016.06.141","article-title":"Improving the Accuracy of Energy Baseline Models for Commercial Buildings with Occupancy Data","volume":"179","author":"Liang","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_2","unstructured":"(2022, October 10). How the 2022 Climate Bill Can Help You Save on Energy Costs. Available online: https:\/\/housemethod.com\/maintenance\/how-the-2022-climate-bill-can-help-you-save-on-energy-costs\/."},{"key":"ref_3","unstructured":"(2022, October 10). Energy Efficiency Directive. Available online: https:\/\/energy.ec.europa.eu\/topics\/energy-efficiency\/energy-efficiency-targets-directive-and-rules\/energy-efficiency-directive_en."},{"key":"ref_4","unstructured":"(2022, October 10). Global Status Report 2017. Available online: https:\/\/www.worldgbc.org\/news-media\/global-status-report-2017."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.apenergy.2014.05.030","article-title":"Uncertainty Estimation Improves Energy Measurement and Verification Procedures","volume":"130","author":"Walter","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.apenergy.2013.04.063","article-title":"Mathematical Description for the Measurement and Verification of Energy Efficiency Improvement","volume":"111","author":"Xia","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.energy.2015.11.077","article-title":"Optimal Metering Plan for Measurement and Verification on a Lighting Case Study","volume":"95","author":"Ye","year":"2016","journal-title":"Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.enbuild.2017.02.040","article-title":"Application of Automated Measurement and Verification to Utility Energy Efficiency Program Data","volume":"142","author":"Granderson","year":"2017","journal-title":"Energy Build."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"117502","DOI":"10.1016\/j.apenergy.2021.117502","article-title":"A Data-Driven Methodology for Enhanced Measurement and Verification of Energy Efficiency Savings in Commercial Buildings","volume":"301","author":"Grillone","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_10","unstructured":"(2012). International Performance Measurement and Verification Protocol, Efficiency Valuation Organization."},{"key":"ref_11","unstructured":"(2014). Measurement of Energy, Demand, and Water Savings, ASHRAE. ASHRAE Guideline 14-2014."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.enbuild.2018.02.023","article-title":"Development and Application of a Machine Learning Supported Methodology for Measurement and Verification (M&V) 2.0","volume":"167","author":"Gallagher","year":"2018","journal-title":"Energy Build."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.apenergy.2016.12.034","article-title":"Cloud Computing Platform for Real-Time Measurement and Verification of Energy Performance","volume":"188","author":"Ke","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Grillone, B., Mor, G., Danov, S., Cipriano, J., Lazzari, F., and Sumper, A. (2021). Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology. Energies, 14.","DOI":"10.3390\/en14175556"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.apenergy.2016.04.049","article-title":"Accuracy of Automated Measurement and Verification (M&V) Techniques for Energy Savings in Commercial Buildings","volume":"173","author":"Granderson","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1016\/j.enbuild.2017.11.039","article-title":"Gradient Boosting Machine for Modeling the Energy Consumption of Commercial Buildings","volume":"158","author":"Touzani","year":"2018","journal-title":"Energy Build."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.tej.2017.08.005","article-title":"The State of Advanced Measurement and Verification Technology and Industry Application","volume":"30","author":"Granderson","year":"2017","journal-title":"Electr. J."},{"key":"ref_18","unstructured":"Effinger, M., and Anthony, J. (2009, January 17\u201319). Case Studies in Using Whole Building Interval Data to Determine Annualized Electrical Savings. Proceedings of the Ninth International Conference for Enhanced Building Operations, Austin, TX, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109807","DOI":"10.1016\/j.enbuild.2020.109807","article-title":"The Use of Artificial Intelligence (AI) Methods in the Prediction of Thermal Comfort in Buildings: Energy Implications of AI-Based Thermal Comfort Controls","volume":"211","author":"Ngarambe","year":"2020","journal-title":"Energy Build."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kumar, N.M., Chand, A.A., Malvoni, M., Prasad, K.A., Mamun, K.A., Islam, F.R., and Chopra, S.S. (2020). Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids. Energies, 13.","DOI":"10.3390\/en13215739"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rathnayaka, P., Moraliyage, H., Mills, N., De Silva, D., and Jennings, A. (2022, January 28\u201331). Specialist vs Generalist: A Transformer Architecture for Global Forecasting Energy Time Series. Proceedings of the 15th IEEE International Conference on Human System Interaction, Melbourne, Australia.","DOI":"10.1109\/HSI55341.2022.9869463"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"722212","DOI":"10.3389\/fenrg.2021.722212","article-title":"Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks","volume":"9","author":"Haputhanthri","year":"2021","journal-title":"Front. Energy Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.14778\/3352063.3352116","article-title":"Data Lake Management: Challenges and Opportunities","volume":"12","author":"Nargesian","year":"2019","journal-title":"Proc. VLDB Endow."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/MIE.2019.2952165","article-title":"Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications","volume":"14","author":"Sierla","year":"2020","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_25","unstructured":"Dull, T. (2022, October 08). Data Lake vs. Data Warehouse: Key Differences. Available online: https:\/\/www.kdnuggets.com\/2015\/09\/data-lake-vs-data-warehouse-key-differences.html."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mills, N., Rathnayaka, P., Moraliyage, H., De Silva, D., and Jennings, A. (2022, January 28\u201331). Cloud Edge Architecture Leveraging Artificial Intelligence and Analytics for Microgrid Energy Optimisation and Net Zero Carbon Emissions. Proceedings of the 15th IEEE International Conference on Human System Interaction, Melbourne, Australia.","DOI":"10.1109\/HSI55341.2022.9869465"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, C.W., Li, C.C., and Lin, C.Y. (2020). Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System. Energies, 13.","DOI":"10.3390\/en13174368"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kahawala, S., Haputhanthri, D., Moraliyage, H., Wimalaratne, S., Alahakoon, D., and Jennings, A. (2022, January 28\u201331). Comparative Evaluation of Gradient Boosting with Active Thresholding and Model Explainability for Peak Demand Forecasting. Proceedings of the 15th IEEE International Conference on Human System Interaction, Melbourne, Australia.","DOI":"10.1109\/HSI55341.2022.9869462"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012057","DOI":"10.1088\/1755-1315\/410\/1\/012057","article-title":"Comparative Study of Measurement and Verification (M&V) Baseline Models for Quantifying Energy Savings in Building Renovations","volume":"410","author":"Cabrera","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_30","first-page":"151","article-title":"Regression Tree Ensembles for Wind Energy and Solar Radiation Prediction","volume":"326\u2013327","author":"Alonso","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"120309","DOI":"10.1016\/j.energy.2021.120309","article-title":"Prediction of Electricity Generation from a Combined Cycle Power Plant Based on a Stacking Ensemble and Its Hyperparameter Optimization with a Grid-Search Method","volume":"227","author":"Qu","year":"2021","journal-title":"Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.apenergy.2015.01.026","article-title":"Automated Measurement and Verification: Performance of Public Domain Whole-Building Electric Baseline Models","volume":"144","author":"Granderson","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s10822-020-00314-0","article-title":"Interpretation of Machine Learning Models Using Shapley Values: Application to Compound Potency and Multi-Target Activity Predictions","volume":"34","author":"Bajorath","year":"2020","journal-title":"J. Comput. Aided. Mol. Des."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111183","DOI":"10.1016\/j.enbuild.2021.111183","article-title":"Measurement and Verification for Multiple Buildings: An Innovative Baseline Model Selection Framework Applied to Real Energy Performance Contracts","volume":"249","author":"Wang","year":"2021","journal-title":"Energy Build."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110990","DOI":"10.1016\/j.rser.2021.110990","article-title":"Review of Data-Driven Energy Modelling Techniques for Building Retrofit","volume":"144","author":"Deb","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Moraliyage, H., Mills, N., Rathnayake, P., De Silva, D., and Jennings, A. (2022, January 28\u201331). UNICON: An Open Dataset of Electricity, Gas and Water Consumption in a Large Multi-Campus University Setting. Proceedings of the 15th IEEE International Conference on Human System Interaction, Melbourne, Australia.","DOI":"10.1109\/HSI55341.2022.9869498"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wimalaratne, S., Haputhanthri, D., Kahawala, S., Gamage, G., Alahakoon, D., and Jennings, A. (2022, January 28\u201331). UNISOLAR: An Open Dataset of Photovoltaic Solar Energy Generation in a Large Multi-Campus University Setting. Proceedings of the 2022 15th International Conference on Human System Interaction (HSI), Melbourne, Australia.","DOI":"10.1109\/HSI55341.2022.9869474"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1080\/23744731.2016.1215199","article-title":"An Hourly Hybrid Multi-Variate Change-Point Inverse Model Using Short-Term Monitored Data for Annual Prediction of Building Energy Performance, Part II: Methodology (1404-RP)","volume":"22","author":"Abushakra","year":"2016","journal-title":"Sci. Technol. Built Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9503\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:34:18Z","timestamp":1760146458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":38,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239503"],"URL":"https:\/\/doi.org\/10.3390\/s22239503","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]}}}