The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings

Results show that models constructed using ML algorithms are more accurate than the conventional approach. A 51.09% reduction in error was achieved using the optimal model algorithm and parameters. The use of a higher measurement frequency reduced the spread of error across the six models. However, further analysis proved the use of more granular data did not always benefit model performance. Results of the sensitivity analysis showed the proposed ML approach to be beneficial in circumstances where missing baseline data limits the model training period length.