Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings
1.1. UK EVs and Power Generation
1.2. EV Infrastructure and Net Profits
1.3. Lifespan and Variables of EVs
1.4. Methods of V2G Charging
1.5. Previous V2G Simulations
2. Proposed Methodology
2.1. The Proposed Methodology
2.2. Machine Learning (ML) Algorithm
3. Results and Discussion
3.1. On-Site Battery Storage
3.2. Charging Stations, EV Variability and Campus Profit
3.3. Net Savings and Charge and Discharge Times
3.4. Energy Consumption Prediction Using ML
3.5. Cost of Electricity Prediction for V2G Using ML
3.6. General Discussion
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|1. Time||2. Price (Pence)||3. Charge/Discharge||4. EV Owner (Pence)||5. Campus (Pence)||6. Campus at Off-Peak Prices (Pence)|
|Time||Price (Pence)||Charge/Discharge||EV Owner (Pence)||Campus (Pence)||Campus at Off-Peak Prices (Pence)|
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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
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/su13074003Chicago/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