From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

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Gallagher, Colm V.
O'Donovan, Peter
Leahy, Kevin
Bruton, Ken
O'Sullivan, Dominic T. J.
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Institute of Electrical and Electronics Engineers (IEEE)
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The European Union's Energy Efficiency Directive is placing an increased focus on the measurement and verification (M&V) of demand side energy savings. The objective of M&V is to quantify energy savings with minimum uncertainty. M&V is currently undergoing a transition to practices, known as M&V 2.0, that employ automated advanced analytics to verify performance. This offers the opportunity to effectively manage the transition from short-term M&V to long-term monitoring and targeting (M&T) in industrial facilities. The original contribution of this paper consists of a novel, robust and technology agnostic framework that not only satisfies the requirements of M&V 2.0, but also bridges the gap between M&V and M&T by ensuring persistence of savings. The approach features a unique machine learning-based energy modelling methodology, model deployment and an exception reporting system that ensures early identification of performance degradation. A case study demonstrates the effectiveness of the approach. Savings from a real-world project are found to be 177,962 +/- 12,334 kWh with a 90% confidence interval. The uncertainty associated with the savings is 8.6% of the allowable uncertainty, thus highlighting the viability of the framework as a reliable and effective tool.
Demand side management , Energy conservation , Learning (artificial intelligence) , Production facilities , Artificial intelligence-based framework , Real-time performance verification , Energy savings , Long-term monitoring , Technology agnostic framework , Machine learning-based energy modelling methodology , Automated advanced analytics , European Union Energy Efficiency Directive , M&V , M&T , Demand-side energy savings , Measurement and verification , Monitoring and targeting , Industrial facilities , Energy 12334.0 kWh , Mathematical model , Uncertainty , Machine learning , Computational modeling , Buildings , Data models , Energy measurement , Performance verification , Energy efficiency , M&V 2.0 , Energy modelling
Gallagher, C. V., O’Donovan, P., Leahy, K., Bruton, K. and O’Sullivan, D. T. J. (2018) 'From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings'. 2018 International Conference on Smart Energy Systems and Technologies (SEST), Sevilla, Spain, 10-12 September. doi: 10.1109/SEST.2018.8495711
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