Impact of weather forecasting uncertainty on building thermal load predictions
Journal
Journal of Building Engineering
Journal Volume
111
Start Page
113366
ISSN
2352-7102
Date Issued
2025-10-01
Author(s)
Abstract
Accurate thermal load prediction is crucial for the efficient operation and control of Heating, Ventilation, and Air Conditioning (HVAC) systems. Weather forecast inaccuracies are a major source of deviation between predicted and actual building thermal loads, potentially compromising HVAC control strategies. However, the influence of these forecasting uncertainties has not been thoroughly quantified. This study conducts a statistical analysis of measured and forecasted weather data to characterize uncertainties in key parameters such as air temperature, relative humidity, solar irradiation, cloud cover, wind speed, and wind direction. The Monte Carlo method is used to evaluate the impact of each parameter's uncertainty on thermal load predictions across various scenarios, including different window orientations, thermal mass levels, shading control strategies, and weather conditions. Results show that forecast errors in solar irradiation and temperature have the greatest influence on thermal load prediction. On a hot sunny day, temperature forecast errors can lead to a 3.69 % overestimation in daily cooling demand for multi-zone spaces and 8.28 % for single-zone spaces. Solar irradiation forecast errors can result in overestimations as high as 30.93 % for multi-zone and 14.48 % for single-zone spaces. However, the influence of solar irradiation errors diminishes when shading devices are implemented.
Subjects
Monte Carlo simulation
Predictive control
Uncertainty modeling
Weather forecasting uncertainty
Publisher
Elsevier BV
Type
journal article