Energy recommender systems attempt to help users attain energy saving goals at home, however previous systems fall short of tailoring these recommendations to users’ devices and behaviors. In this paper we explore the foundations of a user-centered home energy recommendation system. We first conduct a study on a set of recommendations published by utility companies and government agencies to determine the types of recommendations may be popular among typical users. We then design micro-models to estimate energy savings for popular recommendations and conduct a followup study to see if users are likely to carry out these recommendations to achieve estimated savings. We found that users prefer low-cost but potentially tedious recommendations to those that are expensive, however users are unwilling to adopt recommendations that will require long-term lifestyle changes. We also determine that a subset of popular recommendations can lead to substantial energy savings.
Law, M., Thirani, M., Rollins, S., Joshi, A., & Banerjee, N. (2018). Understanding home energy saving recommendations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10809 LNCS, pp. 297–309). Springer Verlag. https://doi.org/10.1007/978-3-319-78978-1_25