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Zero Energy Buildings: From Building Energy Simulation to Indoor Environment...
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Zero Energy Buildings: From Building Energy Simulation to Indoor Environment Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 12651

Special Issue Editors

Dr. Massimiliano Scarpa

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Guest Editor
Department of Architecture and Arts, Università Iuav di Venezia, 30135 Venezia, Italy
Interests: simulation and modeling of buildings and HVAC systems; zero energy buildings; machine learning in building energy performance simulation; building and HVAC system monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Mirco Rampazzo

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, 35122 Padova, Italy
Interests: modeling, identification, and control applications; HVAC&R systems; applications of computational science; industrial artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Angelo Zarrella

E-Mail Website
Guest Editor
Department of Industrial Engineering (DII), Università di Padova, Via Venezia, 1, 35131 Padova, Italy
Interests: energy efficiency of building plant system; nearly zero energy buildings (nZEB); building envelope; radiant systems; high efficiency HVAC integrated systems; thermal comfort; renewable energy; ground source heat pump systems; urban energy modelling; modelling and development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Zero energy buildings (ZEBs) are not only a building design challenge, but also a matter of actual achievement, due to proper management. In fact, the fulfilment of ZEB targets can be greatly hampered by incidental factors such as occupants’ actual behavior, HVAC (heating, ventilation and air-conditioning) system failures, inconvenient regulation of the HVAC system, etc. On the other hand, building energy design should be based on reliable assumptions, for instance, about occupants’ behavior, since wrong boundary conditions may lead to systematically and greatly over/underestimating the energy performance of the building. For this purpose, indoor environment monitoring is crucial. In fact, reliable building energy simulations are pivotal in the assessment of the best building envelope and HVAC system configuration by means of optimization procedures, mainly including financial and energy assessments. Hence, researchers, building management system (BMS) manufacturers, public authorities, building energy designers, software houses, indoor environment assessors, facility managers, etc. are asked to develop calculation procedures, measurement devices and platforms, software, certification systems, building energy design and facility management guidelines aimed at reliably assessing the quality of ZEBs and reducing the gap between building energy design and actual ZEBs’ performance. In this field, this Special Issue of Applied Sciences is dedicated to covering all the activities that may improve the reliability of ZEB design and the achievement of ZEB targets.

Dr. Massimiliano Scarpa
Dr. Mirco Rampazzo
Dr. Angelo Zarrella
Guest Editors

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Keywords

  • Zero energy building
  • Building and HVAC system simulation
  • Optimization in building and HVAC system design
  • Indoor environment monitoring
  • Indoor environment assessment
  • ZEB certification
  • Internal heat gains and occupants’ behavior
  • Machine learning

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Published Papers (4 papers)

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Research

19 pages, 3147 KiB  
Article
Toward ZEB: A Mathematical Programing-, Simulation-, and AHP-Based Comprehensive Framework for Building Retrofitting
by Sobhi Mejjaouli
Appl. Sci. 2022, 12(4), 2241; https://doi.org/10.3390/app12042241 - 21 Feb 2022
Cited by 3 | Viewed by 2165
Abstract
Because of their significant energy consumption and its economic and environmental impacts, existing buildings offer decision makers opportunities and challenges at the same time. In fact, there is a worldwide effort to improve the energy performance of the existing buildings as well as [...] Read more.
Because of their significant energy consumption and its economic and environmental impacts, existing buildings offer decision makers opportunities and challenges at the same time. In fact, there is a worldwide effort to improve the energy performance of the existing buildings as well as the new ones to achieve zero-energy buildings. In this paper, a framework for retrofitting existing buildings to help achieve the goal of zero-energy buildings is presented. The framework details the different steps required to develop and implement a successful retrofitting plan for both residential and commercial buildings. This includes data collection, life cycle cost calculation, building simulation, and multi-criteria decision making using the analytic hierarchy process (AHP). At the end of the paper, a case study is detailed to show the different steps necessary to select a successful retrofitting plan that reflects the decision maker’s objectives. The case study resulted in a retrofitting plan that offers a yearly energy savings of 30% and a payback period of 2.2 years. Full article
(This article belongs to the Special Issue Zero Energy Buildings: From Building Energy Simulation to Indoor Environment Monitoring)
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19 pages, 1553 KiB  
Article
Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages
by Marco Pittarello, Massimiliano Scarpa, Aurora Greta Ruggeri, Laura Gabrielli and Luigi Schibuola
Appl. Sci. 2021, 11(12), 5377; https://doi.org/10.3390/app11125377 - 10 Jun 2021
Cited by 15 | Viewed by 2877
Abstract
Building energy modeling (BEM) is used to support (nearly) zero-energy building (ZEB) projects, since this kind of software represents the only available option to forecast building energy consumption with high accuracy. BEM may also be used during preliminary analyses or feasibility studies, but [...] Read more.
Building energy modeling (BEM) is used to support (nearly) zero-energy building (ZEB) projects, since this kind of software represents the only available option to forecast building energy consumption with high accuracy. BEM may also be used during preliminary analyses or feasibility studies, but simulation results are usually too detailed for this stage of the project. Aside from that, when optimization algorithms are used, the implied high number of energy simulations causes very long calculation times. Therefore, designers could be discouraged from the extensive use of BEM to conduct optimization analyses. Thus, they prefer to study and compare a very limited amount of acknowledged alternative designs. In relation to this problem, the scope of the present study is to obtain an easy-to-use tool to quickly forecast the energy consumption of a building with no direct use of BEM to support fast comparative analyses at the early stages of energy projects. In response, a set of automatic energy assessment tools was developed based on machine learning techniques. The forecasting tools are artificial neural networks (ANNs) that are able to estimate the energy consumption automatically for any building, based on a limited amount of descriptive data of the property. The ANNs are developed for the Po Valley area in Italy as a pilot case study. The ANNs may be very useful to assess the energy demand for even a considerable number of buildings by comparing different design options, and they may help optimization analyses. Full article
(This article belongs to the Special Issue Zero Energy Buildings: From Building Energy Simulation to Indoor Environment Monitoring)
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19 pages, 2298 KiB  
Article
Retrofitted Existing Residential Building Design in Energy and Economic Aspect According to Thailand Building Energy Code
by Santipont Ananwattanaporn, Theerasak Patcharoen, Sulee Bunjongjit and Atthapol Ngaopitakkul
Appl. Sci. 2021, 11(4), 1398; https://doi.org/10.3390/app11041398 - 4 Feb 2021
Cited by 13 | Viewed by 3703
Abstract
Electrical energy usage in buildings is a challenging issue because many old buildings were not originally built to achieve energy efficiency. Thus, retrofitting old buildings to net-zero buildings can benefit both the owner and electric utilities. In this study, the BEC (building energy [...] Read more.
Electrical energy usage in buildings is a challenging issue because many old buildings were not originally built to achieve energy efficiency. Thus, retrofitting old buildings to net-zero buildings can benefit both the owner and electric utilities. In this study, the BEC (building energy code) software was used to evaluate energy aspects of retrofitted buildings in compliance with Thailand’s building energy code to achieve a net-zero energy building. In addition, economic aspects were also studied to verify the feasibility for a project’s owner to invest in a retrofitted existing building. An existing residential building in Thailand was used as a case study. The results in terms of energy after retrofitting existing buildings into net-zero energy buildings show that the total energy consumption can be reduced by 49.36%. From an economic perspective, the investment cost for a retrofitted building can be compensated by energy saving in terms of discounted payback period (DPP) for approximately 4.36 years and has an IRR (internal rate of return) value of 19.23%. This result evidences the potential in both energy and economy for a project’s owner to invest in a retrofitted existing building in compliance with the building code, with potential for implementation with benefits on both electrical utilities and the project’s owner. Full article
(This article belongs to the Special Issue Zero Energy Buildings: From Building Energy Simulation to Indoor Environment Monitoring)
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23 pages, 11039 KiB  
Article
A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic Simulation
by Xavier Godinho, Hermano Bernardo, João C. de Sousa and Filipe T. Oliveira
Appl. Sci. 2021, 11(4), 1356; https://doi.org/10.3390/app11041356 - 3 Feb 2021
Cited by 6 | Viewed by 2572
Abstract
Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In [...] Read more.
Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption. Full article
(This article belongs to the Special Issue Zero Energy Buildings: From Building Energy Simulation to Indoor Environment Monitoring)
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