Cooling load estimation using machine learning techniques

Zingre, Kishor, Srinivasan, Seshadhri and Marzband, Mousa (2019) Cooling load estimation using machine learning techniques. In: ECOS 2019 - 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 23rd - 28th June 2019, Wroclaw, Poland.

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Estimating cooling loads in heating, ventilation, and air-conditioning (HVAC) systems is a complex task. This is mainly due to its dependence on numerous factors which are both intrinsic and extrinsic to buildings. These include climate, forecasts, building material, fenestration etc. In addition, these factors are non-linear and time-varying. Therefore, capturing the effect of these parameters on the cooling load is a complex task. This investigation combines forward modelling, i.e., physics based model simulated using energyPlus with deep-learning techniques to build a cooling load estimator. The forward model captures all the time-varying factors influencing the cooling loads. We use the long short-term memory (LSTM), a deep-learning method to provide forecasts of cooling loads. The advantage of the proposed approach is that cooling load estimations can be provided in real-time thus providing sort of soft-sensor for estimating cooling loads in buildings. The proposed approach is illustrated on a building of suitable scale and our results demonstrates the ability of the tool to provide forecasts.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Heating, Ventilation, and Air-Conditioning (HVAC) Systems, Cooling Load Estimation, Long Short-Term Memory (LSTM), Building Controls, Deep-Learning
Subjects: H600 Electronic and Electrical Engineering
K200 Building
Department: Faculties > Engineering and Environment > Architecture and Built Environment
Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
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Depositing User: Paul Burns
Date Deposited: 11 Sep 2019 15:18
Last Modified: 01 Aug 2021 10:32

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