• The benefits of reducing uncertainty in temperature forecasting can be assessed by VoI • VoI evaluates cost savings in DA energy markets • Accurate temperature modeling can save up to 38.9% in commercial buildings • Accurate temperature modeling can save up to 8.2% in residential buildings • The savings significantly depend on diurnal temperature patterns and building type Urban heat significantly affects human comfort and increases energy consumption for space cooling in built environments. Adaptive decision-making regarding space cooling can reduce the economic cost of a building. In particular, accurate weather forecasts enable operators to predict building energy consumption more precisely and make informed decisions. In this paper, we assess the impact of urban weather forecasts on an energy-related decision in a building using Value of Information (VoI) analysis, a framework to rigorously quantify how reducing uncertainty propagates to informed decisions. To achieve this, we integrate two models: (a) a probabilistic spatiotemporal model for temperature forecasting and (b) a probabilistic model for forecasting the power consumption of a building. By coupling these two models, we quantify the propagating uncertainty from temperature forecasts to load forecasts and assess its influence on adaptive energy purchasing strategy in an energy market, specifically in the day-ahead market, which sets electricity prices based on purchasing timing. Our investigation shows that improved temperature forecasts can reduce energy costs by up to 38.9% in commercial buildings and up to 8.2% in residential buildings, compared to a scenario that does not incorporate temperature forecasts, while the degree of savings varies significantly depending on the building types. The main contribution of this study is to propose a VoI-based framework that quantifies the benefit of reducing uncertainty in building-environment interactive systems, demonstrated through the practical application of uncertainty-aware urban temperature forecasts for optimizing energy decisions in buildings.
Choi et al. (Sat,) studied this question.