Next Article in Journal
Optical Characterization of Alternaria spp. Contaminated Wheat Grain and Its Influence in Early Broilers Nutrition on Oxidative Stress
Next Article in Special Issue
A Circular Economy for the Data Centre Industry: Using Design Methods to Address the Challenge of Whole System Sustainability in a Unique Industrial Sector
Previous Article in Journal
Reduction of Plastic Deformation in Heavy Traffic Intersections in Urban Areas
Previous Article in Special Issue
Discussing the Use of Complexity Theory in Engineering Management: Implications for Sustainability
Article

Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings

Smart Infrastructure and Industry Research Group, Department of Engineering, Manchester Metropolitan University, Chester St., Manchester M1 5GD, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Simon Philbin
Sustainability 2021, 13(7), 4003; https://doi.org/10.3390/su13074003
Received: 7 March 2021 / Revised: 1 April 2021 / Accepted: 1 April 2021 / Published: 3 April 2021
Carbon neutral buildings are dependent on effective energy management systems and harvesting energy from unpredictable renewable sources. One strategy is to utilise the capacity from electric vehicles, while renewables are not available according to demand. Vehicle to grid (V2G) technology can only be expanded if there is funding and realisation that it works, so investment must be in place first, with charging stations and with the electric vehicles to begin with. The installer of the charging stations will achieve the financial benefit or have an incentive and vice versa for the owners of the electric vehicles. The paper presents an effective V2G strategy that was developed and implemented for an operational university campus. A machine learning algorithm has also been derived to predict energy consumption and energy costs for the investigated building. The accuracy of the developed algorithm in predicting energy consumption was found to be between 94% and 96%, with an average of less than 5% error in costs predictions. The achieved results show that energy consumption savings are in the range of 35%, with the potentials to achieve about 65% if the strategy was applied at all times. This has demonstrated the effectiveness of the machine learning algorithm in carbon print reductions. View Full-Text
Keywords: carbon neutral; electric vehicle; vehicle-to-grid; renewable energy; smart charging; net-zero carbon neutral; electric vehicle; vehicle-to-grid; renewable energy; smart charging; net-zero
Show Figures

Figure 1

MDPI and ACS Style

Scott, C.; Ahsan, M.; Albarbar, A. Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings. Sustainability 2021, 13, 4003. https://doi.org/10.3390/su13074003

AMA Style

Scott C, Ahsan M, Albarbar A. Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings. Sustainability. 2021; 13(7):4003. https://doi.org/10.3390/su13074003

Chicago/Turabian Style

Scott, Connor, Mominul Ahsan, and Alhussein Albarbar. 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings" Sustainability 13, no. 7: 4003. https://doi.org/10.3390/su13074003

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop