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Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 27039

Special Issue Editors

Prof. Dr. Gian Marco Revel

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Sciences (DIISM), Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: wearable sensors; sensor network; well-being monitoring; measurement methods; measurement uncertainty; indoor environmental quality; thermal comfort; non-obtrusive monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Arnesano

E-Mail Website
Guest Editor
Department of Theoretical and Applied Sciences, Università eCampus, Via Isimbardi 10, 22060 Novedrate, Italy
Interests: measurements; sensors; IR sensors; wearable sensors; thermal comfort; indoor air quality; buildings monitoring; signal processing; data analysis; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

It is already well established that buildings are responsible for the 40% of the energy consumption around the world, making the construction sector an important target for achieving the objective of reducing human-related environmental impacts. Ambitious and concerted counteractions are crucial, and they should be strategically devised to maximize the energy efficiency but, also, to mitigate the impact of indoor living on climate change. In fact, people are the reason why cities and buildings exist, and occupants do not have energy problems but, instead, comfort problems. As people spend about the 90% of their time indoors, comfort conditions can significantly impact on their wellbeing, health, and productivity. Thus, the monitoring and control of occupants’ comfort should be accurate and comprehensive to facilitate the optimal design and management of buildings while minimizing the required energy expenditure. In this framework, sensing solutions can play an important role. The use of innovative technologies in the field of connectivity and data analysis, such as the IoT and AI, can provide new life to the traditional sensing systems—as applied in the construction sector towards the construction and management of optimal, efficient, and comfortable buildings—as well as be used in behavioral monitoring.

In this context, we are pleased to present the Special Issue “Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management”.

This Special Issue aims at presenting the most advanced sensing technologies to be applied at different stages of the building lifecycle—from the design to operation and maintenance. Contributions are expected to cover a variety of aspects, such as measurements techniques, sensors networks, integration with building management systems, IoT, virtual sensing, and artificial intelligence for enhanced sensing. The contributions could cover both building’s components and occupants, considering the use of wearables and distributed sensors to capture the human comfort and interaction with the building, also applying indoor localization techniques.

The final goal of the Special Issue is to provide knowledge on how innovative sensing solutions can be exploited to improve the construction quality, energy efficiency, and occupants’ comfort and wellbeing in both new and existing buildings.

Prof. Dr. Gian Marco Revel
Dr. Marco Arnesano
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. Sensors 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 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

  • measurements
  • sensors network
  • virtual sensing
  • artificial intelligence for sensing
  • IoT
  • behavioral monitoring in the built environment
  • indoor localization
  • monitoring for building management
  • energy efficiency in buildings
  • indoor environmental quality
  • thermal comfort

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Published Papers (6 papers)

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Research

15 pages, 2949 KiB  
Article
Temperature Sensing Optimization for Home Thermostat Retrofit
by Federico Seri, Marco Arnesano, Marcus Martin Keane and Gian Marco Revel
Sensors 2021, 21(11), 3685; https://doi.org/10.3390/s21113685 - 26 May 2021
Cited by 10 | Viewed by 4246
Abstract
Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap [...] Read more.
Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap and relatively easy-to-install retrofit solutions. These systems provide increased freedom of installation, due to the use of wireless communication; however, the uncertainty of the measured air temperature, considering the thermostat placement, could impact the final heating performance. This paper presents a sensing optimization approach for a home thermostat, in order to determine the optimal retrofit configuration to reduce the sensing uncertainty, thus achieving the required comfort level and minimizing the retrofit’s payback period. The methodology was applied to a real case study—a dwelling located in Italy. The measured data and a simulation model were used to create different retrofit scenarios. Among these, the optimal scenario was achieved through thermostat repositioning and a setpoint of 21 °C, without the use of TRVs. Such optimization provided an improvement of control performance due to sensor location, with consequent energy savings of 7% (compared to the baseline). The resulting payback period ranged from two and a half years to less than a year, depending on impact of the embedded smart thermostat algorithms. Full article
(This article belongs to the Special Issue Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management)
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22 pages, 3508 KiB  
Article
Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization
by Eva Lucas Segarra, Germán Ramos Ruiz and Carlos Fernández Bandera
Sensors 2021, 21(9), 3299; https://doi.org/10.3390/s21093299 - 10 May 2021
Cited by 5 | Viewed by 2630
Abstract
Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the [...] Read more.
Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day. Full article
(This article belongs to the Special Issue Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management)
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20 pages, 6447 KiB  
Article
Probabilistic Load Forecasting for Building Energy Models
by Eva Lucas Segarra, Germán Ramos Ruiz and Carlos Fernández Bandera
Sensors 2020, 20(22), 6525; https://doi.org/10.3390/s20226525 - 15 Nov 2020
Cited by 16 | Viewed by 3765
Abstract
In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact [...] Read more.
In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data. Full article
(This article belongs to the Special Issue Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management)
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20 pages, 10239 KiB  
Article
A Network Sensor Fusion Approach for a Behaviour-Based Smart Energy Environment for Co-Making Spaces
by Teng-Wen Chang, Hsin-Yi Huang, Chung-Wen Hung, Sambit Datta and Terrance McMinn
Sensors 2020, 20(19), 5507; https://doi.org/10.3390/s20195507 - 25 Sep 2020
Cited by 7 | Viewed by 2746
Abstract
User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), [...] Read more.
User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a “Sensible Energy System” (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users’ spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model. Full article
(This article belongs to the Special Issue Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management)
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20 pages, 6940 KiB  
Article
Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches
by Francesco Salamone, Alice Bellazzi, Lorenzo Belussi, Gianfranco Damato, Ludovico Danza, Federico Dell’Aquila, Matteo Ghellere, Valentino Megale, Italo Meroni and Walter Vitaletti
Sensors 2020, 20(6), 1627; https://doi.org/10.3390/s20061627 - 14 Mar 2020
Cited by 28 | Viewed by 5036
Abstract
Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best [...] Read more.
Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99. Full article
(This article belongs to the Special Issue Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management)
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15 pages, 3871 KiB  
Article
Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data
by Amin Ullah, Kilichbek Haydarov, Ijaz Ul Haq, Khan Muhammad, Seungmin Rho, Miyoung Lee and Sung Wook Baik
Sensors 2020, 20(3), 873; https://doi.org/10.3390/s20030873 - 6 Feb 2020
Cited by 57 | Viewed by 7021
Abstract
The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart [...] Read more.
The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis. Full article
(This article belongs to the Special Issue Smart Sensors for Comfortable and Energy Efficient Buildings and Building Management)
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