Harnessing AI and Data Science for the Digital Transformation of Renewable Energy Systems

Sammanfattning

As climate change imposes rapid transformation toward net zero carbon emissions, the transformation of the energy sector poses a key role. With the rapid advancement of artificial intelligence (AI) and its seemingly endless possibilities, it raises the question whether synergies can be realized between AI and the renewable energy sector. The objective of this thesis is to investigate AI and data science applications within the renewable energy sector and provide an overview for energy engineers without previous knowledge of data science and AI. A systematic literature review was conducted to answer these questions. The thesis analyzed 171 peer-reviewed articles and review articles from 2022-2024 using the databases Scopus and Web of Science. Synthesizing the results, three prominent AI application areas were identified; Forecasting, Predictive maintenance and Integration to the grid. ML and ANN represent the most distinguished applications of AI with its speciality on time-series data which is abundant in renewable energy systems and forecasting in particular. Prognostic maintenance applies AI in many different ways ranging from image processing to assess the state of the machinery, to predicting failures with historical data to reduce downtime of power plants. The study further indicates that AI may be particularly well-suited to solve complex nonlinear relationships needed to increase the yield and integrate renewables in the grid. In addition to mapping the current technological landscape of AI, the study presents technical challenges, ethical and societal issues and trends.