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Special Issue "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: 15 April 2021.

Special Issue Editors

Prof. Dr. Gian Marco Revel
Website
Guest Editor
Dipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: non-intrusive measurements; sensors for buildings and industrial applications; sensors for active and assisted living; virtual sensing; thermal comfort
Dr. Marco Arnesano
Website
Guest Editor
Faculty of Engineering, Università eCampus, Via Isimbardi 10, 22060 Novedrate (CO), Italy
Interests: measurements; sensors, IR sensors; wearable sensors; thermal comfort; indoor air quality; buildings monitoring; signal processing; data analysis; energy efficiency

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 papers will be 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 2200 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

Published Papers (4 papers)

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Research

Open AccessArticle
Probabilistic Load Forecasting for Building Energy Models
Sensors 2020, 20(22), 6525; https://doi.org/10.3390/s20226525 - 15 Nov 2020
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
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Open AccessArticle
A Network Sensor Fusion Approach for a Behaviour-Based Smart Energy Environment for Co-Making Spaces
Sensors 2020, 20(19), 5507; https://doi.org/10.3390/s20195507 - 25 Sep 2020
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
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Open AccessArticle
Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches
Sensors 2020, 20(6), 1627; https://doi.org/10.3390/s20061627 - 14 Mar 2020
Cited by 2
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
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Open AccessArticle
Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data
Sensors 2020, 20(3), 873; https://doi.org/10.3390/s20030873 - 06 Feb 2020
Cited by 8
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
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