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Buildings
Special Issues
Prediction and Monitoring of Building Energy Consumption
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Prediction and Monitoring of Building Energy Consumption

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 13735

Special Issue Editors

Prof. Dr. Sergio Vega-Sánchez

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Guest Editor
Escuela Técnica Superior de Arquitectura, Universidad Politécnica de Madrid, Madrid, Spain
Interests: energy efficiency in buildings and cities; energy building retrofitting; BIPV; sustainable facility management; project management; building technical risk management; quality in building construction
Prof. Dr. Beatriz Arranz

E-Mail
Guest Editor
Building Construction Department, Technical University of Madrid (UPM), 28004 Madrid, Spain
Interests: evaluation of indoor environmental quality and sustainability in buildings; comprehensive monitoring of experimental comparative research; designing, building and testing facade component prototypes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the Mission Areas in Horizon Europe is devoted to climate-neutral and smart cities, which has already produced the Mission “100 Climate-neutral Cities by 2030—by and for the Citizens”.

To achieve climate-neutral cities, building energy consumption must be known and minimized. Nowadays, there are discrepancies between prediction stipulated by design and the energy consumed in the stage of use. Different factors cause these discrepancies: mathematic model limitations; climate data; understanding of user behavior; construction quality; building modelling simplification; and urban climate impacts. There are also small mismatches in BEMS and BAS systems due to the differences between daily predictions and actual measured performances in the operation of the buildings. 

The goal of this Special Issue is to publish original contributions on technical, experimental, numerical research, as well as experiences in daily building operations aiming at reducing the gap between prediction and reality and at improving building energy management systems. Topics may include, but are not limited to, the following:

  • Experimental modelling techniques;
  • Innovative modelling procedures and tools;
  • Simplified simulation models;
  • Building operation models;
  • Short-term (daily and weekly) and medium-term (monthly annual) energy use forecasting for building operations;
  • Innovative non-intrusive monitoring techniques for energy efficiency: consumption, contribution of renewables, and hydrothermal comfort;
  • Building operation monitoring;
  • Living lab monitoring;
  • Building component monitoring/test cell monitoring;
  • Gaps between predictions and real monitoring;
  • Adjustment and validation of simulation models based on real data on building performance;
  • Users' influence on real performance;
  • Prediction–monitoring–management: BEMS and BAS;
  • Energy demand levelling;
  • Energy information exchange between stakeholders;
  • Ontologies for energy information management;
  • New construction technologies and energy material characterization;
  • Intelligent construction element characterization;
  • Low-power energy technologies and active solar systems;
  • Urban climatic information accuracy;
  • Urban climatic impact in building simulations results;
  • Impact of climate change in predictions;
  • Climate files and building energy consumption predictions.

Prof. Dr. Sergio Vega-Sánchez
Prof. Dr. Beatriz Arranz
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. Buildings is an international peer-reviewed open access monthly 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 2600 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

  • modelling
  • monitoring
  • gaps between predictions and reality
  • energy efficiency
  • BEMS
  • BAS
  • living labs
  • test cells
  • building innovative components

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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16 pages, 8784 KiB  
Article
Monitoring of a Living Wall System in Santo Domingo, Dominican Republic, as a Strategy to Reduce the Urban Heat Island
by Letzai Ruiz-Valero, Beatriz Arranz, Juan Faxas-Guzmán, Virginia Flores-Sasso, Orisell Medina-Lagrange and Julio Ferreira
Buildings 2023, 13(5), 1222; https://doi.org/10.3390/buildings13051222 - 5 May 2023
Cited by 4 | Viewed by 1830
Abstract
Given the current need to reduce the Urban Heat Island (UHI) worldwide, one of the strategies that can contribute to this mitigation is green façades. In this context, the aim of this research is to evaluate a Living Wall System (LWS) as a [...] Read more.
Given the current need to reduce the Urban Heat Island (UHI) worldwide, one of the strategies that can contribute to this mitigation is green façades. In this context, the aim of this research is to evaluate a Living Wall System (LWS) as a strategy to reduce the urban heat island in Santo Domingo, Dominican Republic, using outdoor test cells. This research was focused on the monitoring of two different façades, an LWS and a reference façade, during the warmer months. For the comparison, the parameters measured were air temperature, relative humidity, surface temperature and environmental variables. In addition, thermal images were taken. Results reveal that during the days selected, the average outdoor air temperature difference between the LWS compared to the reference façade was 5.3 °C, whereas during the day, the average was 3.3 °C. Concerning surface temperature, in the case of the LWS, the temperature was higher and had greater fluctuations than the reference façade. This behavior was confirmed by the results obtained with thermal images. In conclusion, using an LWS in a tropical climate helps the urban microclimate, which contributes to urban heat island effect mitigation during the warmer months. Full article
(This article belongs to the Special Issue Prediction and Monitoring of Building Energy Consumption)
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24 pages, 4302 KiB  
Article
Simplified Model of Heat Load Prediction and Its Application in Estimation of Building Envelope Thermal Performance
by Ziyang Hao, Jingchao Xie, Xiaojing Zhang and Jiaping Liu
Buildings 2023, 13(4), 1076; https://doi.org/10.3390/buildings13041076 - 19 Apr 2023
Cited by 4 | Viewed by 2195
Abstract
This study provides a reference for estimating the building envelope thermal performance at the initial stage of design for nearly zero-energy buildings in different climate zones. A simplified model of heat load prediction, which combines the quasi-steady-state thermal balance calculation procedure in ISO [...] Read more.
This study provides a reference for estimating the building envelope thermal performance at the initial stage of design for nearly zero-energy buildings in different climate zones. A simplified model of heat load prediction, which combines the quasi-steady-state thermal balance calculation procedure in ISO 52016 and the variable-base degree-days method, was proposed. Therefore, a building energy performance evaluation tool BPT V1.0 was developed. Subsequently, the simplified model was validated through comparative analysis with the Building Energy Simulation Test (BESTEST) standard procedure. To conduct a feasibility analysis of the development tool, case studies were performed on the performance evaluation of building envelopes of residential and office buildings in different climate zones in China. Compared to the simulation results from EnergyPlus, the deviation of heat load calculated by BPT V1.0 was within 10%, which further verifies the applicability of the tool under different climatic conditions. Annual heat load under different thermal performance building envelopes was calculated through BPT V1.0. The building energy efficiency improvement rates were found to range from 30 to 60% in nearly zero-energy buildings in different climate zones in China. The study results can provide a reference for energy managers and a basis for estimating the building energy efficiency performance with different envelope thermal properties in the region. Full article
(This article belongs to the Special Issue Prediction and Monitoring of Building Energy Consumption)
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16 pages, 3265 KiB  
Article
Shared PV Systems in Multi-Scaled Communities
by Alina Pasina, Affonso Canoilas, Dennis Johansson, Hans Bagge, Victor Fransson and Henrik Davidsson
Buildings 2022, 12(11), 1846; https://doi.org/10.3390/buildings12111846 - 2 Nov 2022
Cited by 1 | Viewed by 1619
Abstract
In past years, Sweden has been facing a rapid growth of photovoltaic cells, and the total PV installation capacity increased from 300 kW to 1090 MW (2006–2020). The increased number of PV users was a result of active support from the Swedish government [...] Read more.
In past years, Sweden has been facing a rapid growth of photovoltaic cells, and the total PV installation capacity increased from 300 kW to 1090 MW (2006–2020). The increased number of PV users was a result of active support from the Swedish government with an aim of achieving multiple sustainable goals regarding renewable energy. This project evaluates the profitability of shared PV systems in communities of different sizes in Sweden. This study aimed to contribute to the literature by filling the research gap of presenting the financial benefits at different community scales. The electricity use profiles consisted of hourly measured electricity use that was derived from 1067 individual Swedish apartments. The profiles were then used to create multi-scaled communities with shared PV systems. The mid-market price model was implemented to simulate electricity trading among prosumers in the community using Visual Basic Applications (VBA) in MS Excel. Further, the electricity costs were used for Life Cycle Cost (LCC) assessment. To demonstrate the increase in profitability, the LCC results of households with shared PV systems were compared to households that own PV individually and households that do not own a PV system. The evaluation showed the financial benefits of shared PV systems in comparison with individually owned PV systems. This study also demonstrated the increase in profitability and the reduction in payback time for the average household if sharing a PV system as part of a larger community. Full article
(This article belongs to the Special Issue Prediction and Monitoring of Building Energy Consumption)
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35 pages, 13366 KiB  
Article
Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters
by Nicoleta Stroia, Daniel Moga, Dorin Petreus, Alexandru Lodin, Vlad Muresan and Mirela Danubianu
Buildings 2022, 12(7), 1034; https://doi.org/10.3390/buildings12071034 - 18 Jul 2022
Cited by 6 | Viewed by 3829
Abstract
The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power [...] Read more.
The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction). Full article
(This article belongs to the Special Issue Prediction and Monitoring of Building Energy Consumption)
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12 pages, 3385 KiB  
Data Descriptor
Definition of Building Archetypes Based on the Swiss Energy Performance Certificates Database
by Alessandro Pongelli, Yasmine Dominique Priore, Jean-Philippe Bacher and Thomas Jusselme
Buildings 2023, 13(1), 40; https://doi.org/10.3390/buildings13010040 - 24 Dec 2022
Cited by 6 | Viewed by 3120
Abstract
The building stock is responsible for 24% of carbon emissions in Switzerland and 44% of the final energy use. Considering that most of the existing stock will still be in place in 2050, it becomes essential to better understand this source of emissions. [...] Read more.
The building stock is responsible for 24% of carbon emissions in Switzerland and 44% of the final energy use. Considering that most of the existing stock will still be in place in 2050, it becomes essential to better understand this source of emissions. Although the Swiss Cantonal Energy Certificate for Buildings (CECB) database has been used in previous research, no comprehensive characterization of the buildings can be found. This data paper presents an analysis and classification of the Swiss building stock based on the data found in the database. The objective is to create a knowledge foundation that can be used in future research on the performance of existing buildings. Using a sample of almost 50,000 buildings and a Python script, datasheets were created for single-family houses and multi-family houses for nine construction periods. These archetypes are described through selected available energy-related parameters, such as energy reference area, U-values, and energy source with indicators such as median, 25th percentile, and 75th percentile or distributions. The resulting data can be used for different purposes: (1) to calibrate energy models; (2) for analysis that requires scaling-up strategies to the whole stock; and (3) to identify weak and/or relevant classes of buildings throughout the stock. Full article
(This article belongs to the Special Issue Prediction and Monitoring of Building Energy Consumption)
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