Enhance Weather Forecasting, Harnessing the Power of Transformer Neural Networks, for Optimizing Energy as a Service

Background, Description, Delivery and Learning Objective Accurate weather forecasting is critical for the efficient management of energy resources, particularly in the context of Energy-as-a-Service (EaaS) platforms. The ability to anticipate meteorological events and fluctuations in energy production of large scale solar farms enables the optimization of energy delivery, and distribution. Our focus, introduces the latest approach by harnessing the power of transformer neural networks (TNNs) to significantly improve weather forecasting accuracy in a view to advance the optimization of Eaas platforms.

Our work suggests the design of a novel TNN-based architecture, to capture complex patterns and correlations in multiscale spatio-temporal meteorological data. We do this by incorporating external knowledge from domain-specific ontologies, and introducing this data to the TNN, this improves the accuracy of the forecast, allowing for a more effective management of PV solar farms.

Furthermore, we explore the integration of TNNs into EaaS platforms, which we think will allow for a dynamic decision-making in energy production, transportation, and distribution. We believe that the merger will provide increases in efficiencies, reliability, and cost-effectiveness for EaaS providers and end-users alike.

In conclusion, our work showcases the potential of TNNs in weather forecasting, while addressing the growing demand for more efficient energy management applications. Our research serves as a foundation for the development of future platforms using artificial intelligence in the field of meteorology, and energy resource management.