Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments
- PDF / 1,183,279 Bytes
- 17 Pages / 439.37 x 666.142 pts Page_size
- 20 Downloads / 189 Views
Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments Victoria Jayne Mawson1 · Ben Richard Hughes1 Received: 4 December 2019 / Revised: 21 September 2020 / Accepted: 21 September 2020 © The Author(s) 2020
Abstract Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector. Keywords Energy modelling · Manufacturing energy analysis · Energy prediction · HVAC · Building energy analysis · Artificial neural networks
* Victoria Jayne Mawson [email protected] 1
Department of Mechanical Engineering, The University of Strathclyde, Glasgow, UK
13
Vol.:(0123456789)
V. J. Mawson, B. R. Hughes
1 Introduction With increased industrial demand, rising material and energy prices, along with need to ensure product quality and productivity, manufacturing companies are required to develop strategies to reduce the energy consumption of their facility, or face potential penalties from governments for C02 emissions as well as consequences on company image. Facilities also have complex high energy consuming heating, ventilation and air conditioning (HVAC) systems to regulate workshop conditions as well as the high-powered machining equipment. Buildings and the manufacturing industry are responsible for 40% and 42% of the world’s energy consumption respectively (Harish and Kumar 2016; Agency IE 2017). HVAC systems have a reactive based control system, based upon CO2 levels and air temperature, aiming to provide thermal comfort to occupants.
Data Loading...
