English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 widelyaccepted methodologies tend to generalise M&V so as to ensure applicabilityacross the spectrum of energy conservation measures (ECM's). These do notprovide a rigid calculation procedure to follow. This paper aims to bridge thegap between high-level methodologies and the practical application of modellingalgorithms, with a focus on the industrial buildings sector. This is achievedwith the development of a novel, machine learning supported methodology for M&V2.0 which enables accurate quantification of savings.  A novel and computationally efficient feature selection algorithm andpowerful machine learning regression algorithms are employed to maximise theeffectiveness of available data. The baseline period energy consumption ismodelled using artificial neural networks, support vector machines, k-nearestneighbours and multiple ordinary least squares regression. Improved knowledgediscovery and an expanded boundary of analysis allow more complex energysystems be analysed, thus increasing the applicability of M&V. A case study ina large biomedical manufacturing facility is used to demonstrate themethodology's ability to accurately quantify the savings under real-worldconditions. The ECM was found to result in 604,527 kWh of energy savings with57% uncertainty at a confidence interval of 68%. 20 baseline energy models aredeveloped using an exhaustive approach with the optimal model being used toquantify savings. The range of savings estimated with each model are presentedand the acceptability of uncertainty is reviewed. The case study demonstratesthe ability of the methodology to perform M&V to an acceptable standard inchallenging circumstances.

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0