From MV to MT: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

The European Union's Energy Efficiency Directive is placing an increased focus on the measurement and verification (MV) of demand side energy savings. The objective of MV is to quantify energy savings with minimum uncertainty. MV is currently undergoing a transition to practices, known as MV 2.0, that employ automated advanced analytics to verify performance. This offers the opportunity to effectively manage the transition from short-term MV to long-term monitoring and targeting (MT) 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 MV 2.0, but also bridges the gap between MV and MT 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.