Development and application of a machine learning supported methodology
  for measurement and verification (M&V) 2.0
  The foundations of all methodologies for the measurement and verification
(M&V) of energy savings are based on the same five key principles: accuracy,
completeness, conservatism, consistency and transparency. The most widely
accepted methodologies tend to generalise M&V so as to ensure applicability
across the spectrum of energy conservation measures (ECM's). These do not
provide a rigid calculation procedure to follow. This paper aims to bridge the
gap between high-level methodologies and the practical application of modelling
algorithms, with a focus on the industrial buildings sector. This is achieved
with the development of a novel, machine learning supported methodology for M&V
2.0 which enables accurate quantification of savings.
  A novel and computationally efficient feature selection algorithm and
powerful machine learning regression algorithms are employed to maximise the
effectiveness of available data. The baseline period energy consumption is
modelled using artificial neural networks, support vector machines, k-nearest
neighbours and multiple ordinary least squares regression. Improved knowledge
discovery and an expanded boundary of analysis allow more complex energy
systems be analysed, thus increasing the applicability of M&V. A case study in
a large biomedical manufacturing facility is used to demonstrate the
methodology's ability to accurately quantify the savings under real-world
conditions. The ECM was found to result in 604,527 kWh of energy savings with
57% uncertainty at a confidence interval of 68%. 20 baseline energy models are
developed using an exhaustive approach with the optimal model being used to
quantify savings. The range of savings estimated with each model are presented
and the acceptability of uncertainty is reviewed. The case study demonstrates
the ability of the methodology to perform M&V to an acceptable standard in
challenging circumstances.
