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

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.

Automated summary

The study discusses the strategy of utilizing vehicle-to-grid technology to achieve carbon neutral buildings, with investment and implementation of charging stations and electric vehicles to save energy consumption. Through the prediction of machine learning algorithms, energy consumption and costs can be accurately predicted, demonstrating the effectiveness of the algorithm in reducing carbon emissions.