ABSTRACT
This chapter introduces the theories of the most common artificial intelligence (AI) approaches related to the applications in energy systems. It introduces the backgrounds and development of the AI. The chapter discusses the optimisation problems in energy systems and solution approaches. It describes how to use the game theory to model the decision-making and interactions among stakeholders in energy systems. The chapter discusses how to reduce the dimensionality of a high-dimensional dataset by using the principle component analysis (PCA). It discusses the variational inference which is used to approximate intractable integrals. The chapter describes the implementations of the hidden Markov model (HMM) on the problems of the evaluation, learning, and decoding. The game theory serves as an analytical tool for modelling the decision-making and interactions of stakeholders in energy systems. PCA aims to transform a set of linear dependent variables into a set of linear independent variables by using the orthogonal transformation, i.e., reconstructing the original feature space.
