Restriction lift date: 2019-10-13
The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings
| dc.check.date | 2019-10-13 | |
| dc.contributor.author | Gallagher, Colm V. | |
| dc.contributor.author | Bruton, Ken | |
| dc.contributor.author | Leahy, Kevin | |
| dc.contributor.author | O'Sullivan, Dominic T. J. | |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2018-04-27T08:22:58Z | |
| dc.date.available | 2018-04-27T08:22:58Z | |
| dc.date.issued | 2017-10-13 | |
| dc.date.updated | 2018-04-26T11:32:02Z | |
| dc.description.abstract | Accurate energy modelling is a critical step in the measurement and verification (M&V) of energy savings, as a model for consumption in the baseline period is required. Machine learning (ML) algorithms offer an alternative approach to train these models with data-driven techniques. Industrial buildings offer the most challenging environment for the completion of M&V due to their complex energy systems. This paper investigates the novel use of ML algorithms for M&V of energy savings in industrial buildings. This approach enables the extension of the traditional project boundary also. The ML techniques applied consist of bi-variable and multi-variable ordinary least squares regression, decision trees, k-nearest neighbours, artificial neural networks and support vector machines. The prediction performances of the models are validated in the context of a biomedical manufacturing facility to find the optimal model parameters. 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. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Gallagher, C. V., Bruton, K., Leahy, K. and O’Sullivan, D. T. J. (2018) 'The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings', Energy and Buildings, 158, pp. 647-655. doi: 10.1016/j.enbuild.2017.10.041 | en |
| dc.identifier.doi | https://doi.org/10.1016/j.enbuild.2017.10.041 | |
| dc.identifier.endpage | 655 | en |
| dc.identifier.issn | 0378-7788 | |
| dc.identifier.journaltitle | Energy and Buildings | en |
| dc.identifier.startpage | 647 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/5887 | |
| dc.identifier.volume | 158 | en |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2302/IE/Marine Renewable Energy Ireland (MaREI) - The SFI Centre for Marine Renewable Energy Research/ | en |
| dc.subject | Measurement and verification | en |
| dc.subject | Energy efficiency | en |
| dc.subject | Machine learning | en |
| dc.subject | Energy modelling | en |
| dc.subject | Uncertainty analysis | en |
| dc.subject | Energy performance | en |
| dc.title | The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings | en |
| dc.type | Article (peer-reviewed) | en |
