A taxonomy of machine learning applications for virtual power plants and home/building energy management systems

A Virtual power plant is defined as an information and communications technology system with the following primary functionalities: enhancing renewable power generation, aggregating Distributed Energy Resources and monetizing them considering the relevant energy contracts or markets. A virtual power plant also includes secondary functionalities such as forecasting load, market prices and renewable generation, as well as asset management related to the distributed energy ressources. Home energy management systems and building energy management systems have significant overlap with virtual power plants, but these bodies of research are largely separate. Machine learning has recently been applied to realize various functionalities of these systems. This article presents a 3-tier taxonomy of such functionalities. The top tier categories are optimization, forecasting and classification. A scientometric research methodology is used, so that a custom database has been developed to capture metadata from all of the articles that have been included in the taxonomy. Custom algorithms have been developed to generate infographics from the database, to visualize the taxonomy and trends in the research. The paper concludes with a discussion of topics expected to receive a high number of publications in the future, as well as currently unresolved challenges.