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.

[img]
Preview
Text
Zingre et al - Cooling load estimation using machine learning techniques.pdf - Accepted Version

Download (676kB) | Preview

Abstract

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
Related URLs:
Depositing User: Paul Burns
Date Deposited: 11 Sep 2019 15:18
Last Modified: 11 Oct 2019 13:03
URI: http://nrl.northumbria.ac.uk/id/eprint/40640

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence