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Energies
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Improving the Energy Efficiency of Buildings
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Improving the Energy Efficiency of Buildings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 13699

Special Issue Editors

Prof. Dr. Joao Miguel C. Sousa

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Guest Editor
1. School of Technology and Management, Polytechnic Institute of Leiria, Leiria, Portugal
2. INESC Coimbra - Institute for Systems Engineering and Computers at Coimbra, Coimbra, Portugal
Interests: energy efficiency; machine learning; load forecasting; load profiling; energy in buildings
Special Issues, Collections and Topics in MDPI journals
Dr. Paulo Tavares

E-Mail Website
Guest Editor
1. Polytechnic of Coimbra, Coimbra Institute of Engineering, 3030-199 Coimbra, Portugal
2. INESC Coimbra – Institute for Systems Engineering and Computers at Coimbra, 3030-290 Coimbra, Portugal
3. ADAI - Association for the Development of Industrial Aerodynamics, Department of Mechanical Engineering, Pólo II, 3030-788 Coimbra, Portugal
Interests: energy efficiency; automation of buildings; energy management; rational use of energy; sustainable development; buildings energy simulation; passive buildings; electrochromic windows
Special Issues, Collections and Topics in MDPI journals
Prof. Hermano Bernardo

E-Mail Website
Guest Editor
1. School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal
2. INESC Coimbra - Institute for Systems Engineering and Computers at Coimbra, 3030-290 Coimbra, Portugal
Interests: building energy performance modelling, building monitoring, indoor environmental quality; multi-criteria decision aid; demand flexibility; building management and control systems

Special Issue Information

    This Special Issue seeks contributions tackling a wide range of topics addressing improvement of energy efficiency in building approaches, including but not limited to: building energy performance simulation, building automation and control systems (BACS), demand-side flexibility in buildings, post-occupancy evaluation (POE), indoor environmental quality, nearly-zero energy buildings (N-ZEB), smart meter data, multicriteria decision aid approaches in buildings, and energy behavior and sustainability in buildings. Further topics related to machine learning algorithms and data-driven approaches to tackle the challenging topic of analyzing the data gathered from the increasing number of sensors installed in buildings to improve their design and operation are welcome. Interdisciplinary contributions using real-world data will be particularly appreciated.

Keywords

  • energy efficiency and energy savings
  • building energy performance simulation
  • building automation and control systems (BACS)
  • demand-side flexibility in buildings
  • building monitoring and measurements
  • Indoor environmental quality
  • machine learning algorithms and energy performance
  • multicriteria decision aid approaches in buildings
  • occupant-centered approaches
  • sustainability in buildings
  • passive buildings

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Published Papers (5 papers)

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Research

24 pages, 30588 KiB  
Article
Interoperability Testing of a Smart Home Automation System under Explicit Demand Response Schemes
by Nikoleta Andreadou, Evangelos Kotsakis and Marcelo Masera
Energies 2022, 15(21), 7952; https://doi.org/10.3390/en15217952 - 26 Oct 2022
Cited by 1 | Viewed by 2218
Abstract
Interoperability becomes a key issue for smart grid systems, as the interaction between diverse components needs to lead to a normal system operation. In this paper, we test interoperability issues with respect to home automation. In particular, interaction of a home energy management [...] Read more.
Interoperability becomes a key issue for smart grid systems, as the interaction between diverse components needs to lead to a normal system operation. In this paper, we test interoperability issues with respect to home automation. In particular, interaction of a home energy management system (HEMS) is examined with an external actor for home/building remote control. We show the importance and the feasibility of remotely controlling domestic loads from outside the house premises, which can be crucial for energy saving operations, such as demand response. The Smart Grid Architecture Model (SGAM) is used, where the different actors are depicted. The interoperability testing methodology for smart grids, developed by our unit, is followed in order to design the necessary tests and execute them. For the experimental part, we develop an HEMS in our lab along with a Home Automation End Device (HAED), used to transform two normal plugs, and consequently, normal loads into smart ones, thus creating a system for home automation and control. The described configuration is only one possible configuration out of the available ones existing in the market for home automation. LabVIEW programming is used in order to realize the actual explicit demand response program through remote load control and scheduling. The results show that explicit demand response can be achieved by an external actor with success and interoperability is preserved. Full article
(This article belongs to the Special Issue Improving the Energy Efficiency of Buildings)
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20 pages, 10346 KiB  
Article
On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks
by Iivo Metsä-Eerola, Jukka Pulkkinen, Olli Niemitalo and Olli Koskela
Energies 2022, 15(14), 5084; https://doi.org/10.3390/en15145084 - 12 Jul 2022
Cited by 8 | Viewed by 2340
Abstract
Optimizing the heating, ventilation, and air conditioning (HVAC) system to minimize district heating usage in large groups of managed buildings is of the utmost important, and it requires a machine learning (ML) model to predict the energy consumption. An industrial use case to [...] Read more.
Optimizing the heating, ventilation, and air conditioning (HVAC) system to minimize district heating usage in large groups of managed buildings is of the utmost important, and it requires a machine learning (ML) model to predict the energy consumption. An industrial use case to reach large building groups is restricted to using normal operational data in the modeling, and this is one reason for the low utilization of ML in HVAC optimization. We present a methodology to select the best-fitting ML model on the basis of both Bayesian optimization of black-box models for defining hyperparameters and a fivefold cross-validation for the assessment of each model’s predictive performance. The methodology was tested in one case study using normal operational data, and the model was applied to analyze the energy savings in two different practical scenarios. The software for the modeling is published on GitHub. The results were promising in terms of predicting the energy consumption, and one of the scenarios also showed energy saving potential. According to our research, the GitHub software for the modeling is a good candidate for predicting the energy consumption in large building groups, but further research is needed to explore its scalability for several buildings. Full article
(This article belongs to the Special Issue Improving the Energy Efficiency of Buildings)
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29 pages, 5316 KiB  
Article
Interactions between Seismic Safety and Energy Efficiency for Masonry Infill Walls: A Shift of the Paradigm
by André Furtado, Hugo Rodrigues, António Arêde, Fernanda Rodrigues and Humberto Varum
Energies 2022, 15(9), 3269; https://doi.org/10.3390/en15093269 - 29 Apr 2022
Cited by 5 | Viewed by 2255
Abstract
Currently, the upgrade of existing reinforced concrete (RC) buildings focuses only on energy retrofitting measures due to the current policies promoted in the scope of the European Green Deal. However, the structural deficiencies are not eliminated, leaving the building seriously unsafe despite the [...] Read more.
Currently, the upgrade of existing reinforced concrete (RC) buildings focuses only on energy retrofitting measures due to the current policies promoted in the scope of the European Green Deal. However, the structural deficiencies are not eliminated, leaving the building seriously unsafe despite the investment, particularly in seismic-prone regions. Moreover, the envelopes of existing RC buildings are responsible for their energy efficiency and seismic performance, but these two performance indicators are not usually correlated. They are frequently analyzed independently from each other. Based on this motivation, this research aimed to perform a holistic performance assessment of five different types of masonry infill walls (i.e., two non-strengthened walls, two walls with seismic strengthening, and one wall with energy strengthening). This performance assessment was performed in a three-step procedure: (i) energy performance assessment by analyzing the heat transfer coefficient of each wall type; (ii) seismic performance assessment by analyzing the out-of-plane seismic vulnerability; (iii) cost–benefit performance assessment. Therefore, a global analysis was performed, in which the different performance indicators (structural and energy) were evaluated. In addition, a state-of-the-art review regarding strengthening techniques (independent structural strengthening, independent energy strengthening, and combined structural plus energy strengthening) is provided. From this study, it was observed that the use of the external thermal insulation composite system reduced the heat transfer coefficient by about 77%. However, it reduced the wall strength capacity by about 9%. On the other hand, the use of textile-reinforced mortar improved the strength and deformation capacity by about 50% and 236%, but it did not sufficiently reduce the heat transfer coefficient. There is a need to combine both techniques to simultaneously improve the energy and structural energy performance parameters. Full article
(This article belongs to the Special Issue Improving the Energy Efficiency of Buildings)
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21 pages, 3415 KiB  
Article
Smart Thermostats for a Campus Microgrid: Demand Control and Improving Air Quality
by Alexandre Correia, Luís Miguel Ferreira, Paulo Coimbra, Pedro Moura and Aníbal T. de Almeida
Energies 2022, 15(4), 1359; https://doi.org/10.3390/en15041359 - 14 Feb 2022
Cited by 11 | Viewed by 3474
Abstract
Achieving nearly zero-energy buildings (nZEB) is one of the main objectives defined by the European Union for achieving carbon neutrality in buildings. nZEBs are heavily reliant on distributed renewable generation energy sources, which create new challenges associated with their inherent intermittency. To achieve [...] Read more.
Achieving nearly zero-energy buildings (nZEB) is one of the main objectives defined by the European Union for achieving carbon neutrality in buildings. nZEBs are heavily reliant on distributed renewable generation energy sources, which create new challenges associated with their inherent intermittency. To achieve nZEB levels, demand management plays an essential role to balance supply and demand. Since up to two-thirds of the total consumed energy in buildings is dispended for Heating, Ventilation and Air Conditioning (HVAC) operations, intelligent control of HVAC loads is of utmost importance. The present work aims to offer a solution to improve a building microgrids’ flexibility by shifting thermal loads and taking advantage of room thermal inertia. Innovation is present in using the internet of things to link several decentralized local microcontrollers with the microgrid and in the applicability of different control algorithms, such as the pre-emptive heating/cooling of a room. The developed solution relies on smart thermostats, which can be integrated into a building management system, or in a microgrid, and are capable of fulfilling the occupants’ need for comfort while complementing the building with needed power flexibility. The equipment is capable of controlling several HVAC systems to guarantee thermal and air quality comfort, as well as coordinate with a building/microgrid operator to reduce energy costs by shifting thermal loads and enacting demand control strategies. The smart thermostat uses an algorithm to calculate room inertia and to pre-emptively heat/cool a room to the desired temperature, avoiding peak hours, taking advantage of variable tariffs for electricity, or periods of solar generation surplus. The smart thermostat was integrated into a university campus microgrid and tested in live classrooms. Since the work was developed during the COVID-19 pandemic, special attention was given to the air quality features. Results show that smart HVAC control is a viable way to provide occupant comfort, as well as contribute to the integration of renewable generation and increase energy efficiency in buildings and microgrids. Full article
(This article belongs to the Special Issue Improving the Energy Efficiency of Buildings)
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17 pages, 4824 KiB  
Article
Evaluating Insulation, Glazing and Airtightness Options for Passivhaus EnerPHit Retrofitting of a Dwelling in China’s Hot Summer–Cold Winter Climate Region
by Chenfei Liu, Stephen Sharples and Haniyeh Mohammadpourkarbasi
Energies 2021, 14(21), 6950; https://doi.org/10.3390/en14216950 - 22 Oct 2021
Cited by 3 | Viewed by 2092
Abstract
Passivhaus EnerPHit is a rigorous retrofit energy standard for buildings, based on high thermal insulation and airtightness levels, which aims to significantly reduce building energy consumption during operation. However, extra retrofit materials are required to achieve this standard, which raises a contradiction between [...] Read more.
Passivhaus EnerPHit is a rigorous retrofit energy standard for buildings, based on high thermal insulation and airtightness levels, which aims to significantly reduce building energy consumption during operation. However, extra retrofit materials are required to achieve this standard, which raises a contradiction between how to balance the environmental impacts of the retrofitting material inputs and extremely low energy consumption after retrofit. This motivated the analysis in this paper, which aimed to evaluate the possibilities of reducing the required retrofitting material inputs when trying to achieve the EnerPHit energy standard using a typical suburban dwelling in China’s hot summer–cold winter climate region as a case study. Firstly, how the insulation performance of each envelope component affected the building’s energy consumption was analysed. Based on this, sensitivity simulations of combinations of different insulation levels with different fabric components were investigated under four scenarios of insulation levels, airtightness and glazing choice. The final proposed retrofitting plans achieved the EnerPHit standard with insulation materials’ savings between 18% to 58% compared to a baseline retrofit plan, and this led, in turn, to 3.9 to 12.6 tonnes of carbon reductions. Moreover, an energy-saving of 87% in heating and 70% in cooling was achieved compared with the pre-retrofit dwelling. Full article
(This article belongs to the Special Issue Improving the Energy Efficiency of Buildings)
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