Predicting Occupancy Presence in Multiple Resolutions for Commercial Buildings

Sangoboye, Fisayo Caleb; Kjærgaard, Mikkel Baun

doi:10.1145/2993422.2996399

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The inclusion of a hidden state describing the evolution of the effective heat loss coefficient, UA (t) , and the base temperature, T (t) b , has in both cases reduced the systematic errors compared to the static baseline model in Equation (11).…”

Section: Residual Analysesmentioning

confidence: 99%

“…One study demonstrated a method for estimating the number of occupants by using Markov-switching models and CO 2 measurements [10]. Yet another study utilises 3D camera data to estimate occupants' presence [11]. However, less focus has been put on data-driven methods where the human interactions with the building are taken explicitly into account when estimating buildings' thermophysical properties.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Estimation of Time-Varying Stochastic Effects on Building Heat Consumption Related to Human Interactions

et al. 2023

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Within the field of statistical modelling and data-driven characterisation of buildings’ energy performance, the focus is typically on parameter estimation of the building envelope and the energy systems. Less focus has been put on the stochastic human effect on energy consumption. We propose a new method for estimating the thermal building properties while, in parallel, estimating time-varying effects caused by the humans’ interactions with the building. We do that by combining a smooth, non-linear formulation of the energy signature method known from the literature with a hidden state formulated as a random walk to describe the human interactions with the building. The method is demonstrated on data obtained from autumn 2019 to late spring 2021 from a 900 m2 newly built school building located south of Oslo, Norway. The demonstration case has shown that the model accuracy increases and the model bias decrease when cross-validated. The estimated hidden state has also been shown to resemble the estimated combined mechanical and natural ventilation pattern controlled by the building users and operational staff. These human interactions have increased the total heat loss expressed in kilowatts per kelvin by around 50% over the course of one year from before the COVID-19 pandemic to after its outbreak.

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Section: Residual Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%