From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings
Gallagher, Colm V.
O'Sullivan, Dominic T. J.
Institute of Electrical and Electronics Engineers (IEEE)
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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