Quantifying and simulating the weather forecast uncertainty for advanced building control

Publication Type

Journal Article

Date Published

01/28/2025

Authors

DOI

Abstract

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modeling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen-Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

Journal

Journal of Building Performance Simulation

Year of Publication

2025

URL

ISSN

1940-1493, 1940-1507

Organization

Research Areas

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