Exploring the Potential of Emerging Digitainability—GPT Reasoning in Energy Management of Kindergartens

Abstract

One of the barriers to the rapid transition of societies toward a more sustainable future is a scarcity of field experts. Members of scientific and professional communities believe that this obstacle could be overcome by supplementing the decisions of non-experts with artificial intelligence. To examine this opportunity, this study examines the viability of GPT-3.5 as an expert adviser in the energy management of kindergartens. Thus, field experts investigated the deductive and inductive reasoning potential of GPT-LLM (Large Language Model). The first task was conducted on a sample of kindergartens in the Western Balkans. The LLM was instructed to provide the buildings’ specific heat consumption (SHC) by relatively detailed building descriptions and building occupancy. The second task involved kindergartens in various European locations, and the LLM was tasked with estimating energy savings using limited data about the renovation process. The study found deductive reasoning to be insufficient for estimating SHC from the building envelope details, with average accuracy below the least predictive model (R2 = 0.56; MAPE = 48%). Including the factor of occupancy, the SHC estimates were relatively accurate, wherein the first deductive test proved precise (MAPE = 27%), but it was less so in the opposite case (MAPE = 67%). In terms of inductive reasoning, the LLM assumptions were relatively consistent with practice.