**1. Introduction**

The future of power generation will be dominated by harvesting energies from renewable sources and methods for reducing CO<sup>2</sup> emissions. This is ensured through government regulations with, e.g., the UK's net-zero greenhouse gas (GHG) target by 2050 [1]. As the demand for energy increases, the reliability of the generation and distribution network of national grids (NGs) decreases, due to intermittent renewable energy sources. This can be unpredictable, as the supply can be low when the renewable generation is high, and the volatility of renewable generation can create an unpredictable demand from the NG. In France, the decrease in fossil fuels leads to an increase in renewables, mostly wind, which leads to the greater import of energy and a decrease in grid consistency [2].

To compensate for the inefficiency of renewable power supply installations, Vehicle to grid (V2G) can be used. In the V2G method, the batteries from charged electric vehicles (EV) can be discharged back into the grid at peak times. It can be connected to the grid, or it can be utilised by a business, which can choose to store the energy, sell to the national grid, or use it. Peak power plants are used when the demand exceeds what is expected [3]. These plants can quickly generate the required energy on top of the already generated energy of the NG, but they are not environmentally friendly, with the fuel being sourced by gas or diesel generating CO<sup>2</sup> as a bio-product. Therefore, it is required to develop a way of excess peak power generation without the use of fossil fuels.

**Citation:** 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

Academic Editor: Simon Philbin

Received: 7 March 2021 Accepted: 1 April 2021 Published: 3 April 2021

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