Artificial Intelligence Techniques for Building-to-Building Renewable Energy Sharing: A Synthesis Study
Building-to-building renewable energy sharing (B2B RES) involves exchanging excess renewable energy between buildings. Artificial Intelligence (AI) techniques, including machine learning algorithms, neural networks, and multi-agent systems (MAS), have been proposed to help, to coordinate and to optimize energy flows in B2B RES systems. This paper reviews and compares the capabilities and limitations of these AI techniques for B2B RES, and presents a case study on the application of a MAS approach in a smart grid scenario. The authors suggest that the MAS approach is suitable for B2B RES because it is decentralized, flexible, and it enables autonomous decision-making and cooperation among agents representing buildings or energy-related entities. It can also adapt to changing conditions and uncertainties in the energy market, and allow for the representation of different stakeholders and preferences in B2B RES. Further research is needed to validate and improve the performance of the MAS approach in real-world B2B RES systems.
Keywords - AI techniques, B2B RES, Machine Learning, Neural Networks, Multi Agent Systems, energy sharing.