Search

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort. [ABSTRACT FROM AUTHOR]

This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches. [ABSTRACT FROM AUTHOR]

Energy is at the center of most of the major challenges of our society, but equally at the center of the promising perspectives that the world is facing. In fact, the combination of wind, water and solar energy can replace depleting fossil fuels which produce greenhouse gases on the one hand, and on the other hand cover the entire energy needs of the planet. Solar energy is both clean and non-polluting, moreover it is one of the most popular solutions for households as an alternative to fossil fuels. Therefore, the use of photovoltaic systems reduces the energy supplied by the public grid and inevitably decreases the amount of the energy invoice in the long term. Due to the increase in the number of bachelor students in recent years, the Ibn Tofail University in Kenitra, Morocco, has been required to build new buildings and laboratories to ensure a good study environment for its students. This has led to an enormous increase in the electricity bill. In this context, our project aims to decrease the energy bill of the two institutions: National School of Applied Sciences (ENSA) as well as National School of Commerce and Management (ENCG), by studying the technical and economic aspects of photovoltaic installations for self-consumption purposes, as well as the installation of solar streetlamps to improve the performance of nocturnal lighting. In addition, an initiation to the intelligent smart building concept that allows the centralized technical management of the installed equipment, and more precisely the DALI lighting protocol, was extended. Indeed, this system will allow us to control the consumption of the loads remotely and in real time for a better comfort and without neglecting the reduction of the energy bill. [ABSTRACT FROM AUTHOR]

ZigBee and LoRa are communication technologies widely used in the application of the Internet of things (IoT), especially in the field of smart building environmental monitoring. The main purpose of this study is to compare and analyze the transmission performance of ZigBee and LoRa wireless communication networks in the building. Through two indicators of packet loss rate (PLR) and round-trip time (RTT), this paper discusses in detail the transmission performance of ZigBee and LoRa technologies in whole buildings under the same working conditions. We set up three experimental scenarios of line-of-sight, horizontal and vertical to evaluate the communication performance of these two networks by changing the baud rate and packet length, and cost and power consumption were considered. Experiments have shown that LoRa networks outperform ZigBee networks in most cases and are the best choice for building communication networks. The experimental results provide basic data support and engineering reference for the application of these two technologies in buildings, especially for the deployment of communication networks throughout buildings. The innovation and contribution of this paper are to discuss the effect of packet length, baud rate, distance, and different locations within a building on the performance of ZigBee and LoRa transmissions, using RTT and PLR as metrics through three experimental scenarios. [ABSTRACT FROM AUTHOR]

Smart building techniques have drawn considerable attention for the potential to accommodate renewable energy resources and achieve local energy sustainability. However, information privacy, growing number of users and vulnerability of communication channels have led to energy management and cyber-security issues. In this paper, a cyber-attack resilient energy management system for smart residential buildings is proposed. An aggregative game is formulated to describe the non-cooperative interactions among smart buildings that are coupled through dynamic electricity pricing. The existence of the variational equilibrium is analyzed theoretically. Moreover, a semi-decentralized algorithm is employed to attain the equilibrium solution without disclosing private information of individual buildings. To detect potential price attacks, a cyber-attack detection mechanism is proposed by comparing the predicted and actual electricity costs and exchanged power based on the long short-term memory network model. Numerical simulations demonstrate the performance of the semi-decentralized algorithm in term of convergence speed and robustness against disturbances. Additionally, the effectiveness of the resilient energy management strategy is investigated. [ABSTRACT FROM AUTHOR]

While there is an increasing integration of connected devices in buildings to reduce energy consumption, little evidence exists regarding the effective environmental impact of the smart devices installed. One goal of this study is to comparatively assess the impact of integrating connected devices on the environmental performances of existing buildings. Additionally, it aims to discuss the added value regarding environmental performance between a strategy of integrating connected devices and a refurbishment strategy. A non-refurbished connected building will thus be compared with a non-connected refurbished building. The approach's novelty lies in carrying out an uncertainty analysis, which addresses uncertainties in the environmental modelling of sensors, heating gains, and other influential building parameters, such as occupancy scenarios and building lifespan. The Life Cycle Assessment model is built on the results of a dynamic building energy simulation and integrates environmental data on connected devices from ecoinvent. Environmental indicators are calculated using ImpactWorld+. The methodology is applied to a multi-storey residential tower in France, characterised by a low insulation level. The alternatives compared include the original building without sensors, two connected alternatives (monitoring and management) that lead to a reduction of heating loads by 10 and 30% respectively, and a refurbished alternative. Results confidently show that using connected devices is always better than keeping the original building state. Yet, refurbishing the building is the preferable option across almost all indicators. Finally, the results point out that energy gains allowed by connected devices should be better quantified in further work through measurement campaigns. • Comparative assessment of conventional, connected, and refurbished building alternatives is carried out. • Stochastic comparative LCAs are performed to identify the best alternative. • Energy gains allowed by connected buildings are not offset by the additional devices. • Despite uncertainties, energy management offers superior benefits over monitoring in reducing environmental impacts. • The refurbished alternative has the fewest environmental impacts. [ABSTRACT FROM AUTHOR]

As new algorithms incorporate occupancy count information into more sophisticated HVAC control, these technologies offer great potential for reductions in energy costs while enhancing flexibility. This study presents results from a two-year field evaluation of an occupancy-based HVAC control system installed in an office building. Two wings on each of the building’s 2–11 floors were equipped with occupancy counters to learn occupancy patterns. In combination with proprietary machine learning algorithms and thermal modeling, the occupancy data were leveraged to implement optimized start, early closure, and adjustments to fan operation at the air handling unit (AHU) level. This study conducted a holistic evaluation of technical performance, cost-effectiveness analysis, and user satisfaction. Results show the platform reduced weekday AHU run times by 2 h and 35 min per AHU per day during the pandemic time period. Simulation shows that 6.1% annual whole-building savings can be achieved when the building is fully occupied. The results are compared with prior studies, and potential drivers are discussed for future opportunities. The assessment results shed light on the expected in-the-field performance for researchers and industry stakeholders and enabled practical considerations as the technology strives to move beyond research-grade pilot trials into product-grade deployment.

This work addresses the problem of quantifying opacity for discrete event systems. We consider a passive intruder who knows the overall structure of a system but has limited observational capabilities and tries to infer the secret of this system based on the captured information flow. Researchers have developed various approaches to quantify opacity to compensate for the lack of precision of qualitative opacity in describing the degree of security of a system. Most existing works on quantifying opacity study specified probabilistic problems in the framework of probabilistic systems, where the behaviors or states of a system are classified as secret or non-secret. In this work, we quantify opacity by a state-worth function, which associates each state of a system with the worth it carries. To this end, we present a novel category of opacity, called worthy opacity, characterizing whether the worth of information exposed to the outside world during the system’s evolution is below a threshold. We first provide an online approach for verifying worthy opacity using the notion of a run matrix proposed in this research. Then, we investigate a class of systems satisfying the so-called 1-cycle returned property and present a worthy opacity verification algorithm for this class. Finally, an example in the context of smart buildings is provided.

Project manager competencies have been widely discussed in the literature. Although many works present lists of competencies for project managers, specific competencies for smart buildings were not found. This work aimed to identify the main competencies of project managers for the management of smart building projects. The methodology consisted of a two-step approach using the guidelines of preferred reporting items for systematic reviews and meta-analysis (PRISMA) to perform broad and detailed bibliographic research. Initially, a literature review was carried out to identify the main research fields related to the management of smart building projects and the specificities of these fields that can influence the management of this type of project. Next, these specificities were related to the competencies of project managers mentioned in the literature, to identify a set of more relevant competencies for project managers of smart building projects. The results showed that the main research fields are related to scope, risks, costs, schedule, technologies and systems, sustainability, and general characterization, which consider objectives, barriers, challenges, and regulations. They also showed that the most relevant competencies for managing smart buildings projects are technical competencies, leadership, communication, budgeting, attitudes toward risk, strategic management, organization, and specifying real requirements. These findings show the importance of hard and soft skills for managing smart building projects.

In this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer; (ii) it is assumed a single CP for the entire building; (iii) an energy resource manager entity is considered to manage the energy resources in the residential building, such as Electric Vehicles (EVs), Photovoltaic (PV) generation system, and the Battery Energy Storage System (BESS). Taking into consideration the previous assumptions, the major goal of this work is to minimize the electricity consumption costs of the residential building by using a Multi-Objective Mixed-Binary Linear Programming (MOMBLP) formulation. The objective function of the MOMBLP model minimizes the electricity cost consumption of each apartment. Then, a Goal Programming (GP) strategy is applied to find the most appropriate solutions for the proposed MOMBLP model. Finally, the performance of the suggested model is evaluated by comparing the obtained results from a Single-Objective Mixed-Binary Linear Programming (SOMBLP) approach in which the whole building consumption cost is minimized. The results show that using the GP strategy a reduction of 7.5% in the total annual energy consumption is verified in comparison with SOMBLP. Moreover, the GP approach leads to fair benefit among building consumers, by finding a solution with less distance from the desired level.

The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy.

Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand Heating, Ventilation, and Air Conditioning (HVAC) systems is presented to demonstrate the use of PBD.

Indoor environmental quality (IEQ) is a critical aspect of the built environment to ensure occupant health, comfort, well-being and productivity. Existing IEQ monitoring approaches rely on sensor networks deployed at selected locations to collect environmental measurements, and are limited in scale and adaptability due to infrastructure cost and maintenance requirement. To enable high-granularity IEQ monitoring with agile adaption to the dynamic indoor environment, we propose an “automated mobile sensing” system that dispatches a sensor-rich navigation-capable robot to actively survey the indoor space. Data collected in this fashion is sparse in the joint temporal and spatial domain, and cannot be used directly for IEQ evaluation. To deal with this special characteristics, we developed a spatio-temporal interpolation algorithm to capture the global trend and local variation in order to use the data efficiently to reconstruct the IEQ dynamics. We compared the performance of the automated mobile sensing with a dense sensor network in a laboratory where we measured the air-change effectiveness (ASHRAE standard 129) for four different conditions. Results indicate that automated mobile sensing is able to accurately estimate the parameters with a minimal sensor cost and calibration effort. Potential applications of this system include indoor thermal comfort, lighting, indoor air quality and acoustic monitoring, pollutant source identification, and building commissioning. We shared publicly the source codes for robot control, sensor setup, and interpolation algorithm to encourage comparison study and further development.

Buildings shape cities as those cities grow from and nurture people living and working within the built environment. Thus, the conceptualization of smart building should be brought closer to the smart city initiatives that particularly target ensuring and enhancing the sustainability and quality of urban life. In this paper, we propose that a smart building should be interlinked with a smart city surrounding it; it should provide good experiences to its various occupants and it should be in an ongoing state of evolving as an ecosystem, wherein different stakeholders can join to co-produce, co-provide and co-consume services. Smart buildings require a versatile set of smart services based on digital solutions, solutions in the built environment and human activities. We conducted a multiphase collaborative study on new service opportunities guided by a Design Thinking approach. The approach brought people, technology, and business perspectives together and resulted in key service opportunities that have the potential to make the buildings smart and provide enjoyable experience to the occupants who support their living and working activities in smart cities. This paper provides the resulting practical implications as well as proposes future avenues for research.

In this study, a metaheuristic-based method, called Balanced Water Strider Optimization Algorithm (BWSOA), is presented to predict the thermal variables of a building. The measurement of these variables is not easy and the determination of them by features of materials is difficult. Also, it depends on the building type and the structure of the building The variable prediction problem is calculated as a single-objective optimization problem and the DesignBuilder™ is utilized to design the thermal model, which is a black-box here. The simulated and practical energy usage data of the building is utilized to show the operation of the suggested model by employing the model to two various geographical conditions placed in New Zealand. The algorithm performance is more showed by assuming simulated energy usage data; made after subjecting the thermal model of the building to a simulated geographical environment of another city (Tauranga); with the considerable various weather condition in comparison to that of Wellington. The obtained results stated that the suggested method can estimate these variables efficiently. Moreover, this approach can be efficient to develop energy management systems for the building.

Fire modelling is a common practice in building fire safety analysis, but it is costly. This work develops an AI software, Intelligent Fire Engineering Tool (IFETool), to speed up the fire safety analysis and quickly identify design limits. A big numerical atrium-fire database is firstly formed by considering key building and fire parameters. Then, a deep learning model is trained to predict the evolution of tenable smoke visibility, temperature and CO concentration with an accuracy of 97%. The tenability descending profile is further processed to assess the available safe egress time (ASET) and the fire safety of the atriums that have complex roof shapes and slab extensions. This AI design software is able to make a quick assessment of the proposed atrium fire engineering design and give valuable suggestions for potential improvement. Finally, the operation guidelines of IFETool are provided for common design tasks of atrium fire safety.

With the support of advanced measurement technologies and regulation terminals,demand-side flexible resources such as electric vehicles and air-conditioning loads in smart buildings have good power response potential in real time and can be aggregated to form virtual power plants to participate in frequency regulation auxiliary service of the system. To protect the needs of users when virtual power plants participate in frequency regulation auxiliary services,a control strategy for smart building-based virtual power plants is proposed in this paper. Firstly,the frequency regulation operation mode of the virtual power plant is analyzed,and a typical demand-side resource model and virtual energy storage model of the smart building-based virtual power plants are established. Secondly,considering the uncertainty of frequency regulation signal and user side,a day-ahead optimal scheduling model based on opportunity constrained planning is established. Then the real-time power allocation is realized based on the principle of cost optimization. Finally,the effectiveness and feasibility of the frequency regulation strategy of the virtual power plants proposed in this paper are verified by simulation calculations,and the joint scheduling of demand-side resources can improve the revenue of the virtual power plants while safeguarding the needs of users.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In large buildings, during the situation of fire or other hazards, a smart evacuation guidance system needs to fully consider manifold aspects of hazards to guide evacuees through exit gates as fast as possible by dynamic and safe routes. In this paper, we propose a smart evacuation guidance system with a dynamic evacuation routing approach by using the LCDT (Length-Capacity-Density-Trustiness) weighted graph model and partial view (PV) information which represents the hazard intensity and the crowd congestion information of a group of sections/floors in the building. The proposed system is designed as a distributed system with multiple layers of computing by using smart indicators. Given such a system, we develop an efficient distributed approach, so-called LCDT&PV, to find out effective evacuation routes dynamically. We then propose an estimating congestion strategy in order to improve the efficiency of dynamic evacuation routes. To validate the proposed system, we implement a simulator to compare the proposed evacuation routing approach with baseline approaches. Experimental results show that the proposed approach reduces up to 30% of the total evacuation time compared with others. Furthermore, through the results of initial smart indicator implementation, which can interact with the simulator, we show the viability of the proposed system. [ABSTRACT FROM AUTHOR]

High-quality data are always desirable for superior decision-making in smart buildings. However, latency issues, communication failures, meter glitches, etc., create data anomalies. Especially, the redundant/duplicate records captured at the same time instants are critical anomalies. Two such cases are the same timestamps with the same energy consumption reading and the same timestamps with different energy consumption readings. This causes data inconsistency that deludes decision-making and analytics. Thus, such anomalies must be properly identified. So, this paper performs an enumeration of redundant data anomalies in smart building energy consumption readings using an analytical approach with 4-phases (sub-dataset extraction, quantification, visualization, and analysis). This provides the count, distribution, type, and correlation of redundancies. Smart buildings' energy consumption dataset of Darmstadt city, Germany, was used in this study. From this study, the highest count of redundancies is observed as 5060 on 26 January 2012 with the average count of redundancies at the hour level being 211 and the minute level being 7. Similarly, the lowest count of redundancies is observed as 89 on 24 January 2012. Further, out of these 5060 redundancies, 1453 redundancies are found with the same readings and 3607 redundancies are found with different readings. Additionally, it is identified that there are only 14 min out of 1440 min on 26 January 2012 without having any redundancy. This means that almost 99% of the minutes in the day possess some kind of redundancies, where the energy consumption readings were recorded mostly with two occurrences, moderately with three occurrences, and very few with four and five occurrences. Thus, these findings help in enhancing the quality of data for better analytics. [ABSTRACT FROM AUTHOR]

The latest progress in information and communication technology (ICT) and the Internet of Things (IoT) have opened up new opportunities for real-time monitoring and controlling of cities' structures, infrastructures, and services. In this context, smart water management technology provides the data and tools to help users more effectively manage water usage. Data collected with smart water devices are being integrated with building management systems to show how much water is used by occupants as well as to identify the consumption areas to use water more efficiently. By this approach, smart buildings represent an innovative solution that enhances a city's sustainability and contributes to overcoming environmental challenges due to increasing population and climate change. One of the main challenges is resource-saving and recovery. Water is an all-important need of all living beings, and the concerns of its scarcity impose a transition to innovative and sustainable management starting from the building scale. Thus, this manuscript aims to provide an updated and valuable overview for researchers, consumers, and stakeholders regarding implementing smart and sustainable technologies for water resource management, primarily for building-scale uses. [ABSTRACT FROM AUTHOR]

This forward-looking perspective article presents a status overview of solar photovoltaic-thermal (PVT) panels in net-zero energy buildings from various points of view and tries to picture the future of the technology in this framework. The article discusses the pros and cons of PVTs' state of practice, design developments, and integration possibilities. Investigations show that for sufficiently benefiting from the potential of PVT panels for smart buildings, some major challenges such as high investment cost and lack of two-way interaction with district energy systems must be addressed. In addition, some of the most promising research focuses of the field are discussed as the further possible solutions for advancing the state-of-the-art in this context. These are finding feasible ways to reduce the cost of PV cells, downsizing battery and heat pumps based on optimal two-way interactions with thermal and power grids, tri-generating via enabling the panel for passive cooling (PVTC), and developing concentrating PVTs and PVTCs. The potential impact of this article's advice for future research may be an increased motivation of buildings to be furnished by such solar-based energy systems and thus a higher contribution of PVT panels in net-zero and smart energy buildings. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a huge impact on society. Scientists are working to mitigate the impact in many ways. As a field closely related to human life, building engineering can make a great contribution. In this article, we started with the concept of the smart building as our guide. The impact of COVID-19 on daily energy consumption, information and communication technology, the ventilation of the interior environment of buildings, and the higher demand for new energy technologies such as electric vehicles is an entry point. We discuss how the concept of the smart building and related technologies (refrigeration, measurement, sensor networks, robotics, local energy generation, and storage) could help human society respond to the pandemic. We also analyze the current problems and difficulties that smart buildings face and the possible future directions of this technology. [ABSTRACT FROM AUTHOR]