CiC(SEA)-Based FaaS (Forecast-as-a-Service) Framework to Enhance Effective and Widespread Utilization of Renewable Energy Sources

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

Accurate and affordable forecasting is essential for effective and widespread utilization of renewable energy sources such as wind power and solar energy. The objective of this research is to develop a Forecast-as-a-service (FaaS) framework that (1) generates accurate forecasts that integrate prediction results from different models using data from different sources; and (2) provides on-demand delivery of different types of forecasts at different levels of details for different prices. Forecasting activities and business workflows are modeled by applying the concept of services, composite services, and the principles of service-oriented architecture. Implemented by using the Azure platform, the FaaS framework involves the orchestration of service activities performed by a Forecast Generator Framework, an Internal Data Retrieval Framework and an External Data Collection Framework. Potential users of the FaaS framework include renewable energy users and providers, power system operators, and potential renewable energy users/producers that are at different stages of planning for new facilities. The FaaS framework can contribute to the filling of two national needs ? energy independence and environmental stewardship. It can help power companies in many states to meet their respective state mandates in renewable portfolio standards. The FaaS framework can become a part of a national forecast cyber-infrastructure.