Markov Chain model for energy community member reaction to recommendations: a multi-agent case study

Energy communities represents a potential way to guide people towards being more attentive when consuming electricity. In such a community, people may voluntary participate and provide a certain contribution, with the aim to obtain a collective benefit. We believe that, under the guidance of an intelligent recommendation system, people may improve their behaviour and consume energy in a more efficient manner. However, since human energy consumption behaviour is unpredictable, developing simulation tools and models that could explore the opportunity of such a concept could be difficult. Therefore, we propose a Markov chain model that is used to represent the behaviour of people in a multi-agent simulation, under the guidance of a recommendation system. Results show that even under uncertainty, a recommendation system may provide a certain environmental/economical benefit.