Temp-AI-Estimator: Interior Temperature Prediction using Domain-Informed Deep Learning

Approximately 40% of total energy demand in the European Union is consumed by the residential buildings sector, thus also significantly contributing to carbon dioxide emissions. Circa 28% of this energy demand is attributed to space heating and cooling, primarily influenced by the building’s envelope and need to ensure indoor thermal comfort. Given this significant energy consumption, there is an urgent imperative to explore energy-saving strategies and develop tools to assess the effects of various design alternatives, with a focus on wall and roof characteristics. While existing white and black-box predictive models lack generalisation capabilities, the goal of this study is to develop and train a domain-informed grey-box Deep Learning model called Temp-AI-Estimator to predict the indoor temperature of buildings and containers (acting as surrogates for residential or office buildings as well as intermodal …