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Sustainable Future-Proofing of Heating and Cooling in Buildings: Heat Pumps, Passive Measures, and Low-Carbon Interventions

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Green Building".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 3769

Special Issue Editors

Dr. Ali Badiei

E-Mail Website
Guest Editor
School of Engineering, Faculty of Science and Technology, University of Central Lancashire, Preston PR1 2HE, UK
Interests: energy simulation and performance analysis; building physics and energy evaluation; solar-assisted heat pump technologies; low-carbon cooling technologies
Dr. Arash Beizaee

E-Mail Website
Guest Editor
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: thermal comfort; indoor environmental quality; building performance modelling; low-energy building services; energy flexibility and resilience in the built environment
Dr. Jing Li

E-Mail Website
Guest Editor
Energy and Environment Institute, University of Hull, Hull HU67RX, UK
Interests: heat pump; solar thermal conversion; concentrated solar power; thermal storage; organic rankine cyle; refrigeration cycle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The latest IPCC report (Intergovernmental Panel on Climate Change, 2022) confirms the strong interactions of the natural, social and climate systems, and that human-induced climate change has caused widespread adverse impacts to nature and people. It is clear that across sectors and regions, the most vulnerable people and systems are disproportionately affected, and climate extremes have led to irreversible impacts. The assessment underscores the importance of limiting global warming to 1.5°C if we are to achieve a fair, equitable and sustainable world. One of the key contributors to increasing worldwide energy consumption is the service sector, which covers all types of buildings with a wide range of HVAC systems. The global energy consumption of the service sector has increased by 295 Mtoe in 2018 compared to 2000 levels, and with this trend, the sector would consume a further 323 Mtoe by 2040.

The increase in the global temperatures and carbon emissions, accompanied by recent geo-political developments, has triggered drastic measures by most governments to reduce the dependency of heating and cooling in buildings on fossil fuels. As a result, new policies are being introduced to boost technological advances, which could speed up the sustainable future-proofing of heating and cooling in buildings. We are at a critical point in history where we need to act decisively to encourage energy savings while protecting vulnerable households, and prepare smart energy programs.

This Special Issue of Sustainablity, “Sustainable Future-proofing of Heating and Cooling in Buildings: Heat Pumps, Passive Measures, and Low-Carbon Interventions”, is focused on new methodologies, low-carbon strategies and technologies, passive cooling and heating measures, and heat pumps to reduce the fossil fuel-reliance of heating and cooling in buildings. We invite researchers to contribute original research articles as well as review articles that will stimulate the continuing efforts to understand the recent advances and innovation in these research fields. Manuscripts combining experimental implementation with theoretical calculations and technoeconomic assessment are also welcome.  

Themes and topics for this Special Issue include:

  • Sustainable transition to low-carbon heating and cooling in buildings;
  • Passive measures to reduce demand and fossil fuel dependency in buildings;
  • Low-energy cooling technologies and strategies;
  • Technological advances in heat pump development;
  • Solar-assisted and building-integrated heat pump technologies;
  • Occupants’ comfort, health and wellbeing in a changing climate;
  • Energy flexibility and resilience in the built environment;
  • Personal comfort systems and other alternative low energy heating and cooling technologies;
  • 2050 road maps and pathways to develop net-zero buildings.

Dr. Ali Badiei
Dr. Arash Beizaee
Dr. Jing Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable
  • buildings
  • heat pumps
  • low-carbon
  • low-energy
  • passive
  • flexibility
  • resilience
  • thermal comfort
  • occupancy patterns
  • alternative technologies

Published Papers (3 papers)

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Research

22 pages, 10100 KiB  
Article
Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls
by Xin Xiao, Qian Hu, Huansong Jiao, Yunfeng Wang and Ali Badiei
Sustainability 2023, 15(14), 11365; https://doi.org/10.3390/su151411365 - 21 Jul 2023
Viewed by 962
Abstract
The outdoor thermal environment can be regarded as a significant factor influencing indoor thermal conditions. The application of phase change materials (PCMs) to the building envelope has the potential to improve the heat storage performance of building walls and, therefore, effectively regulate the [...] Read more.
The outdoor thermal environment can be regarded as a significant factor influencing indoor thermal conditions. The application of phase change materials (PCMs) to the building envelope has the potential to improve the heat storage performance of building walls and, therefore, effectively regulate the temperature variations of the inner surfaces of walls. COMSOL Multiphysics software was adopted firstly to perform the simulations on the thermoregulation performance of phase change wall; the time duration of the temperature at the internal side maintained within the thermal comfort range was used as a quantitative evaluation index of the thermoregulation effects. It was revealed from the simulation results that the time durations of thermal comfort were extended to 5021 s and 4102 s, respectively, when the brick walls were filled with two types of composite PCMs, namely eutectic hydrate (EHS, Na2CO3·10H2O and Na2HPO4·12H2O with the ratio of 4∶6)/5 wt.% BN and EHS/5 wt.% BN/7.5 wt.% expanded graphite (EG), under the conditions of 18 °C ambient temperature and 60 °C heating temperature at the charging stage. Both of them were longer than 3011 s, which corresponds to a pure brick wall. EHS/5 wt.% BN/7.5 wt.% EG exhibited better leakage prevention performance and, therefore, was a candidate for actual application, in comparison with EHS/5 wt.% BN. Then, a machine learning training process focused on the temperature control effects of phase change wall was carried out using a BP neural network, where the heating surface and ambient temperature were used as input variables and the time duration of indoor thermal comfort was the output variable. Finally, the learning deviation between the raw data and the results obtained from machine learning was within 5%, indicating that machine learning can accurately predict the temperature control effects of the phase change wall. The results of the simulations and machine learning can provide information and guidance for the advantages and potentials of PCMs of hydrate salts when being applied to the building envelope. In addition, the accurate prediction of machine learning demonstrated its application prospects to the research of phase change walls. Full article
(This article belongs to the Special Issue Sustainable Future-Proofing of Heating and Cooling in Buildings: Heat Pumps, Passive Measures, and Low-Carbon Interventions)
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22 pages, 4573 KiB  
Article
The Energy-Saving Potential of Air-Side Economisers in Modular Data Centres: Analysis of Opportunities and Risks in Different Climates
by Ali Badiei, Eric Jadowski, Saba Sadati, Arash Beizaee, Jing Li, Leila Khajenoori, Hamid Reza Nasriani, Guiqiang Li and Xin Xiao
Sustainability 2023, 15(14), 10777; https://doi.org/10.3390/su151410777 - 10 Jul 2023
Viewed by 1152
Abstract
This study examines the feasibility of utilising outside air for ‘free cooling’ in modular data centres through the implementation of an air-side economiser, as an alternative to traditional mechanical cooling systems. The objective is to offset the energy consumption associated with cooling by [...] Read more.
This study examines the feasibility of utilising outside air for ‘free cooling’ in modular data centres through the implementation of an air-side economiser, as an alternative to traditional mechanical cooling systems. The objective is to offset the energy consumption associated with cooling by leveraging the natural cooling capacity of the ambient air. To investigate this potential, a 90-kW modular data centre is employed as the base case for model validation and analysis of energy reduction possibilities. The research employs dynamic thermal modelling techniques to assess the efficacy of the air-side economiser in four distinct climatic zones: Stockholm, Dubai, San Francisco, and Singapore, representing diverse worldwide climates. The model is meticulously calibrated and validated using power usage effectiveness (PUE) values obtained from the Open Compute Project. Simulation runs are conducted to evaluate the energy-reduction potential achievable with the air-side economiser compared to conventional mechanical air-conditioning systems. The results indicate significant energy reductions of up to 86% in moderate climates, while minimal reductions are observed in dry and hot climates. This comprehensive analysis offers valuable insights into the intricate relationship between modular data centres, their operational characteristics, and the viability of employing air-side economisers for free cooling and energy efficiency across different climatic conditions. The contribution of this publication to this field of science lies in its exploration of the practicality and energy-saving potential of air-side economisers in modular data centres. By utilising dynamic thermal modelling and empirical validation, this study provides evidence-based insights into the effectiveness of this cooling strategy, shedding light on its applicability in various climates. The findings contribute to the understanding of energy-efficient cooling solutions in data-centre design and operation, paving the way for more sustainable practices in the field. Full article
(This article belongs to the Special Issue Sustainable Future-Proofing of Heating and Cooling in Buildings: Heat Pumps, Passive Measures, and Low-Carbon Interventions)
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23 pages, 8012 KiB  
Article
Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption
by Yu Cui, Zishang Zhu, Xudong Zhao and Zhaomeng Li
Sustainability 2023, 15(11), 8750; https://doi.org/10.3390/su15118750 - 29 May 2023
Cited by 3 | Viewed by 979
Abstract
Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is [...] Read more.
Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance. Full article
(This article belongs to the Special Issue Sustainable Future-Proofing of Heating and Cooling in Buildings: Heat Pumps, Passive Measures, and Low-Carbon Interventions)
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