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Energy management is one of the main challenges in Microgrids (MGs) applied to Smart Buildings (SBs). Hence, more studies are indispensable to consider both modeling and operating aspects to utilize the upcoming results of the system for the different applications. This paper presents a novel energy management architecture model based on complete Supervisory Control and Data Acquisition (SCADA) system duties in an educational building with an MG Laboratory (Lab) testbed, which is named LAMBDA at the Electrical and Energy Engineering Department of the Sapienza University of Rome. The LAMBDA MG Lab simulates in a small scale a SB and is connected with the DIAEE electrical network. LAMBDA MG is composed of a Photovoltaic generator (PV), a Battery Energy Storage System (BESS), a smart switchboard (SW), and different classified loads (critical, essential, and normal) some of which are manageable and controllable (lighting, air conditioning, smart plugs operating into the LAB). The aim of the LAMBDA implementation is making the DIAEE smart for energy saving purposes. In the LAMBDA Lab, the communication architecture consists in a complex of master/slave units and actuators carried out by two main international standards, Modbus (industrial serial standard for electrical and technical monitoring systems) and Konnex (an open standard for commercial and domestic building automation). Making the electrical department smart causes to reduce the required power from the main grid. Hence, to achieve the aims, results have been investigated in two modes. Initially, the real-time mode based on the SCADA system, which reveals real daily power consumption and production of different sources and loads. Next, the simulation part is assigned to shows the behavior of the main grid, loads and BESS charging and discharging based on energy management system. Finally, the proposed model has been examined in different scenarios and evaluated from the economic aspect.

The paper investigates the energy performances of several configurations of solar-assisted heat pumps equipped with photovoltaic (PV) and photovoltaic-thermal (PVT) panels as well as solar thermal collectors for domestic hot water production. The systems are sized to serve small aggregates of users assumed as micro energy communities in low housing density context in the South of Italy. Electricity storages are also considered to smooth the interaction with the electric grid. Several indices are defined and calculated to guide toward a correct sizing, to check users’ satisfaction and to assess primary energy savings. The results show how the energy consumption from the analysed systems is mainly influenced by the amount and structure of domestic hot water demand per day. Also, according to the chosen plant layouts, it is shown that thermal contribution of PVT slightly influences the system performances. Better results in terms of less non-renewable primary energy consumption are recorded for the PV-based plant. Solar thermal collectors, even if sized for high solar fractions, do not properly support the daily heat pump operation, especially in winter, and present a certain electricity consumption for auxiliaries which results to be penalizing. Additional calculations were performed for the case of energy communities in a context with higher uses of non-renewable energy for electricity generation. Finally, opportunities to support demand-response actions for grid electricity management were investigated.

Heating, Ventilation, and Air Conditioning (HVAC) are extremely energy-consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants’ thermal comfort. We first design a deep Feedforward Neural Network (FNN) based approach for predicting the occupants’ thermal comfort, and then propose a Deep Deterministic Policy Gradients (DDPG) based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simu- lation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants’ thermal comfort by 13.6%. National Research Foundation (NRF) Accepted version This research is supported in part by a project fund from DSAIR@NTU, and a BSEWWT project fund from Na- tional Research Foundation Singapore, administrated through the BSEWWT program office (Ref. BSEWWT2017_2_06), the Green Buildings Innovation Cluster (Grant NO.: NRF2015ENC-GBICRD001-012), administered by Building and Construction Authority (BCA) Singapore.

_ Perkembangan teknologi meningkat pesat seiring berjalannya waktu. Mulai dari mobil pintar yang bisa berjalan sendiri ke berbagai tujuan tanpa pengemudi, hingga perangkat rumah pintar yang bisa secara otomatis bersuara mengingatkan untuk melakukan aktifitas sesuai jadwal. Semua teknologi terbaru ini adalah bagian dari Internet of Things. Internet of Things (IoT) adalah konsep dimana objek memiliki kemampuan untuk mentransfer data melalui jaringan tanpa memerlukan interaksi dari manusia ke manusia atau dari manusia ke komputer. IoT adalah struktur dimana, orang diberi identitas eksklusif dan kemampuan untuk memindahkan data melalui jaringan tanpa memerlukan dua arah antara manusia ke manusia, sumber ke tujuan atau interaksi antara manusia dan komputer. Jadi, penelitian ini bertujuan untuk mengidentifikasi dan menjelaskan penerapan sistem IoT dalam mendukun g program smart building dan lingkungan yang terintegrasi. Penelitian ini dianalisis secara deskriptif kualitatif dengan pendekatan studi kasus untuk mengeksplorasi manfaat teknologi IoT. Hasil penelitian ini menunjukkan dengan menghubungkan sistem bangunan yang berbeda dengan sensor dan perangkat IoT yang terhubung ke jaringan, aplikasi bangunan pintar untuk lingkungan perumahan pintar, dan ekosistem dapat terintegrasi menggunakan data lintas operasi, memberikan visibilitas yang lebih besar, wawasan cepat, dan pengalaman penghuni yang lebih baik. Teknologi IoT dapat bekerja dengan baik jika interaksi antara perangkat IoT dengan manusia atau pengguna harus berjalan dengan baik, karena IoT tidak akan berfungsi secara optimal jika pengguna tidak dapat mengoperasikannya dengan baik. Kata kunci: Internet of Things (IoT) ; Bangunan Pintar; Lingkungan Perumahan Pintar; Ekosistem Terpadu, Sistem Bangunan. Abstract _ Technological development increases rapidly over time. Starting from smart cars that can walk alone to various destinations without a human driver, to smart home devices that can automatically remind the voice to do activities on schedule. All of this latest technology is part of the Internet of Things. Internet of Things (IoT) is a concept where objects can transfer data through networks without requiring interaction from human to human or from human to computer. IoT is a structure in which objects, people are given an exclusive identity and the ability to move data through networks without requiring two directions between humans to humans, that is, sources to destinations or interactions between humans and computers. So, this research aims to identify and explain the application of the IoT system in support of smart building programs and integrated environments. This study was analyzed descriptively qualitative with a case study approach to explore the benefits of IoT technology. The results of this study indicate by connecting disparate building systems with networked IoT sensors and devices, smart building applications for smart housing environments and integrated ecosystems can use data across operations, providing greater visibility, valuable insights, and better occupant experiences. IoT technology can work well if the interaction between IoT devices with humans or users must go well because the function of IoT will not work optimally if the user cannot operate it properly. Keywords: Internet of Things (IoT); Smart Building; Smart Housing Environment; Integrated Ecosystems, Building Systems.

Two-way communication facilities and advanced metering infra-structure enable residential buildings to be capable of actively participating in demand side management schemes. This paper proposes a new home energy management system (HEMS), which optimally schedules the operation of home energy resources, with the aim to minimize the home’s one-day electricity cost charged by the real-time pricing while taking into account the monthly basis peak power consumption penalty, charged by the demand charge tariff. To better ensure the user’s lifestyle requirements, the HEMS also models lifestyle-related operational dependencies of household appliances. Numerical simulations and case studies are conducted to validate the reasonability of the proposed method. Australian Research Council

Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Publisher Copyright: © 2021 The Authors The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings’ adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation.

Sustainable and smart building is a recent concept that is gaining momentum in public opinion, and thus, it is making its way into the agendas of researchers and city authorities all over the world. To move towards sustainable development goals, 5G technology would make significant impacts are building construction, operation, and management by facilitating high-class services, providing efficient functionalities. It's well known that the Singapore is one of top smart cities in this world and from the first counties that adopted of 5G technology in various sectors including smart buildings. Based on these facts, this paper discusses the international trends in 5G applications for smart buildings, and R&D and test bedding works conducted in 5G labs. As well as, the manuscript widely reviewed and discussed the 5G technology development, use cases, applications and future projects which supported by Singapore government. Finally, the 5G use cases for smart buildings and build environment improvement application were discussed. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data.

Presently, due to the establishment of a sensor network, residual buildings in urban areas are being converted into smart buildings. Many sensors are deployed in various buildings to perform different functions, such as water quality monitoring and temperature monitoring. However, the major concern faced in smart building Wireless Sensor Networks (WSNs) is energy depletion and security threats. Many researchers have attempted to solve these issues by various authors in different applications of WSNs. However, limited research has been conducted on smart buildings. Thus, the present research is focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. In the grid-based model, a grid organizer was selected based on the sailfish optimization algorithm. Subsequently, a fuzzy expert system is used to select the relay node to reach the shortest path for data transmission. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A simulation study of the proposed framework was conducted to evaluate its performance. Some metrics, such as the packet delivery ratio, end-end delay, and average residual energy, were calculated for the proposed model. The average residual energy for the proposed framework was 96%, which demonstrates the effectiveness of the proposed routing design.

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarmintelligence- based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal. post-print 888 KB

Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

Due to the fast evolution of Sensor and Internet of Things (IoT) technologies, several large-scale smart city applications have been commercially developed in recent years. In these developments, the contracts are often disputed in the acceptance due to the fact that the contract specification is not clear, resulting in a great deal of discussion of the gray area. Such disputes often occur in the acceptance processes of smart buildings, mainly because most intelligent building systems are expensive and the operations of the sub-systems are very complex. This paper proposes SpecTalk, a platform that automatically generates the code to conform IoT applications to the Taiwan Association of Information and Communication Standards (TAICS) specifications. SpecTalk generates a program to accommodate the application programming interface of the IoT devices under test (DUTs). Then, the devices can be tested by SpecTalk following the TAICS data formats. We describe three types of tests: self-test, mutual-test, and visual test. A self-test involves the sensors and the actuators of the same DUT. A mutual-test involves the sensors and the actuators of different DUTs. A visual-test uses a monitoring camera to investigate the actuators of multiple DUTs. We conducted these types of tests in commercially deployed applications of smart campus constructions. Our experiments in the tests proved that SpecTalk is feasible and can effectively conform IoT implementations to TACIS specifications. We also propose a simple analytic model to select the frequency of the control signals for the input patterns in a SpecTalk test. Our study indicates that it is appropriate to select the control signal frequency, such that the inter-arrival time between two control signals is larger than 10 times the activation delay of the DUT.

Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset. Smart Facility Management with IoT.

Smart Building/Environment management is an interesting topic that, although widely investigated in the literature, has not seen wide adoption in real case studies. Current Smart Building solutions provide facilities for: i) automatically managing HVAC (Heating, Ventilation and Air Conditioning) systems; ii) energy management, iii) building automation. Current solutions are tightly coupled with the underlying IT infrastructure of the building and cannot be rearranged according to the events, catastrophic or otherwise, that may modify the building structure (e.g., a disruption to sections of a building). In this work, we present a novel approach for Smart Buildings (we refer to it as "Software Defined Building 2.0") to customize and reprogram IT infrastructure powering buildings, this way enabling cooperation among Cyber-Physical Systems (CPSs) operating within. We analyze an architecture enabling this approach, evaluating overhead management and its impact on systems’ performance.

International audience; We present the results for a new building supply and communication architecture based on Power Over Ethernet (PoE) with the IEEE 802.3bt standard. Driven by technological advances like photovoltaic production, LED lighting terminals, computer terminals, tablets, screens and the advancement of power electronics that allow more and more conversions with very high efficiency, direct current in buildings might be the right solution to reduce energy consumption and go towards positive energy buildings. With the new IEEE 802.3bt PoE standard which allows 99 W and with the increasing demand for new services in smart buildings and the emerging of IoT devices, we propose a communication and supply architecture completely based on the IP protocol. This paper presents feedback of the first deployment of a smart building totally powered by PoE devices, we analyse the power supply architecture and the application communication protocol that we have used which is MQTT (Message Queuing Telemetry Transport).

International audience; The aim of the work presented in this paper is the energy management in residential microgrids. The study focuses on Multi Agent System approach based on sensors and actuators network. The system operation requires a large amount of data both for its architectural description and for its real time control. Dealing with this, the fundamental step in the energy management is the organization of data in order to construct the suitable relational database model. The second step is to analyze the system architecture and identify the agents to use for the system. When data are structured and Multi Agent System is modelled, the energy management algorithms are developed and validated taking into account the data availability and the agents properties.

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC, 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is further developed for data monitoring on a smartphone. All in all, this self-powered WSN node can interface with the users of the SHBs for displaying ambient data, which demonstrates its promising applicability and stability.

The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research subjects, and the environmental parameters of the dwellings’ main activity rooms and the blood pressure parameters of the older adults were measured. Based on the Long Short-Term Memory (LSTM) deep learning algorithm and Bayesian fitting method, a hypertension disease model was established using the long-term environmental parameters to predict the hypertension risk of older adults in their building’s environment. The results showed that temperature, humidity, and some air quality parameters had an impact on blood pressure under single environmental factor, and the comprehensive environmental risks of high systolic blood pressure, high diastolic blood pressure, and high blood pressure were 16.44%, 0%, and 16.44% for the male elderly and 14.11%, 7.14%, and 17.55% for the female elderly, respectively. By comparing the results for the blood pressure measurement and prediction, it can be observed that the risk error of hypertension obtained by the algorithm maintains the variables’ relationship, and the result of the algorithm is reliable in this period. This technology can provide a basis for measuring environmental parameters and will be conducive to the development of an ecological smart building environment.

This paper presents the design of a modular and open-source smart meter for educational and research purposes. The smart meter monitors the energy consumption, uploads the data to a cloud server, and can be used to control appliances remotely through a control relay. Using the Internet of Things (IoT) technology, the smart meter acts as a sensing node in a meshed network. This enables consumers to visualize their energy consumption profiles at the level of appliances and to take control actions to manage efficient energy consumption using the automation platforms or IoT with smart meters. In this paper, the energy consumed by different electrical appliances is computed and communicated to a server using the Arduino Uno board aided by the ESP8266 WiFi module. The energy consumption is also displayed on an LCD screen, and the consumption profiles are displayed through the home energy monitoring website "EmonCMS." The design can accommodate different transducer ratings with single-phase and three-phase measurements by configuring itself automatically using the plug-and-play approach. The realization of the proposed smart meter is a multicourse educational project that aims to polish students' skills in applying the knowledge gained in Electrical Engineering (ie, undergraduate courses) to produce a practical application. This project was made possible by UREP grant # UREP19-017-2-007 and by NPRP grant NPRP(10-1203-160008)from the Qatar National Research Fund (a member of Qatar Foundation). Scopus

The electricity output of combined heat and power (CHP) units is constrained by their heat output corresponding to customers’ heat demand, which makes it difficult for the CHP units to frequently adjust their electricity output. Therefore, additional balancing power is required to integrate the variable wind power in the CHP-based heat and electricity integrated energy system (HE-IES). This paper expands the demand response (DR) concept to the HE-IES. A comprehensive DR strategy combining energy substitution and load shifting is first developed to exploit the demand flexibility of smart buildings. Besides electric balancing power, heat balancing power is also provided to relax the production constrains of CHP units. Moreover, a real-time DR exchange (DRX) market is developed where the building aggregators are stimulated to adjust buildings’ energy consumption behaviors and provide the required balancing power. Compared with the existing day-ahead DRX market, the real-time DRX market can balance the very short-term wind power fluctuation and reduce price spikes. Additionally, a novel optimum feasible region method is proposed to achieve the fast clearing of the DRX market to meet the higher requirement for clearing speed in the real-time market. Simulation results verify the advantages of the proposed technique.

The economic dispatch (ED) operation is based on determining the optimal output of all the distributed energy resources (DERs) of a microgrid to meet the load demand at the lowest cost. In this paper, a smart commercial building model is supplied by several distributed generations (DGs), for example, PV power plants, diesel generators, and energy storage system (ESS). In addition, the microgrid is connected to the main grid (grid-connected mode), which allows the system to trade power with the main grid. The aim of this paper is managing DERs that operate over a time horizon and satisfy several key constraints at the lowest possible cost. Therefore, an improved energy management (EM) operation is proposed to achieve better energy efficiency in the building. In the proposed EM operation, the ED problem is investigated according to several aspects, such as the impact of air temperature, thermal resistance (R) of the building envelope, and time horizon (day-ahead ED and five-minute-ahead ED). Finally, the results of the general algebraic modeling system (GAMS) are used to validate the accuracy and feasibility of the proposed EM operation.

The application of Internet of Things in smart buildings is becoming more and more extensive. The energy supply problem of end nodes has always been a concern of the field of research and industry areas, and an adaptive radio frequency energy conversion system is proposed for the low power node of the Internet of Things. By using the unipolar transistor control, the threshold voltage of the rectifier changes dynamically, through RF matching, and double voltage rectification measures the supply for the low power nodes. The proposed circuit is aimed at reducing the threshold voltage of the transistor forward bias to increase power and output voltage of the harvester and increasing the reverse bias of the threshold voltage for reducing the leakage current. It prevents the loss of stored energy. This paper presents a 12 stages adaptive threshold-compensated rectifier circuit has reached the maximum power conversion efficiency 35.3% at −14 dBm. Meanwhile, when the input power is −12dBm, the output voltage is 3.2 V at the output load 1 $\text{M}\Omega $ . The low power characteristics of the nodes at the end of the Internet of Things make it possible to convert the RF energy into electric energy.

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

International audience; In this paper we introduce a dataset of environmental information obtained via indoor and outdoor sensors deployed in the SMART Infrastructure Facility of the University of Wollongong (Australia). The acquired dataset is also made open-sourced along with this paper. We also propose a novel approach based on an evolutionary algorithm to determine pairs of correlated sensors. We compare our approach with three other standard techniques on the same dataset: on average, the accuracy of the evolutionary method is about 62,92%. We also evaluate the computational time, assessing the suitability of the proposed pipeline for real-time applications.

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others., European Commission, Junta de Comunidades de Castilla-La Mancha, Agencia Estatal de Investigación

Typically, residential buildings neither allow flexibility in the individual contract power capacity nor considers buildings as unique electricity consumers. In this work, a smart building is designed that each electricity customer has flexible contract power and the whole collective residential building has a single contract power. A management entity is considered to manage all energy resources of the building such as the photovoltaic generation, electric vehicles, and battery energy storage system, taking into consideration the consumption from apartments and common services, to minimize the electricity bill. Hence, the best/optimal contract power capacity will contribute to minimizing electricity costs. Therefore, finding the optimal decision of the contract power value has received a significant role from the energy management in smart buildings. In this paper, a mixed binary optimization problem is formulated in which not only the optimal value of contract power is yield but the optimal schedule of the electric vehicle/battery storage charge and discharge are found, taking into consideration the photovoltaic generation and load consumption profiles. The proposed model is implemented for three scenarios, and the obtained results show that the model efficiency has a high performance with a significant electricity cost reduction, around 47%. The results pointed that using an optimal value of single contract power and intelligent management system, the building electricity costs decrease remarkably., This work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project BENEFICE-PTDC/EEI-EEE/29070/2017 and UIDB/00760/2020 under CEECIND/02814/2017 grant.

The development of the smart building concept and building automation field is based on the exponential evolution of monitoring and control technologies. Residents of the smart building must interact with the monitoring and control system. A widely used method is specific applications executed on smartphones, tablets, and PCs with Bluetooth connection to the building control system. At this time, smartphones are increasingly used in everyday life for payments, reading newspapers, monitoring activity, and interacting with smart homes. The devices used to build the control system are interconnected through a specific network, one of the most widespread being the Building Automation and Control Network (BACnet) network. Here, we propose the use of the BACnet Application Layer over Bluetooth. We present a proposal of a concept and a practical implementation that can be used to test and validate the operation of the BACnet Application Layer over Bluetooth.

In recent years, the growing number of devices connected to the internet has increased significantly. These devices can interact with the external environment and with human beings through a wide range of sensors that, perceiving reality through the digitization of some parameters of interest, can provide an enormous amount of data. All this data is then shared on the network with other devices and with different applications and infrastructures. This dynamic and ever-changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications based on IoT have been developed, think of Smart Cities, smart roads, and smart industries. This article analyzes the current architectures, technologies, protocols, and applications that characterize the paradigm.

According to EU's energy efficiency and climatic change prevention strategy, one of the largest accents in smartening the grid for high penetration of distributed and intermittent renewable energy sources (RESs) is now being made on buildings. Buildings are the largest end-use sector taking about 40% of total final energy consumption and 55% of electricity consumption in the EU-28 in 2012. It is considered as one of the most prospective platforms when utilised smartly to solve discrepancies from mismatch between intermittent energy generation and demand. Referring to the energy performance of Buildings Directive, the flexibility of a building’s overall electricity demand, including its ability to enable participation in demand response (DR) is highlighted as one of three main smart grid readiness indicators and key functionalities of buildings. However, flexibility is easy to define but not easy to quantify. This paper aims to quantify the flexibility, namely the actual amount of peak-hour energy that can be shaved through DR application in active residential building equipped with deferrable/controllable energy system units such as a heat pump (HP) and in combination with hot water storage tank (HWST). The advantage of this study is a detailed HP energy model including restarting delay, energy conversion delay, defrost mode, guaranteed power-on duration, aimed to maximally reflect its operating behaviour. The computation of flexibility is realized using a rule-based management strategy to provide a response to a technical (incentive-based and reliability-oriented) DR signal without violating user thermal comfort preferences when following all the aforementioned limitations. Two different storage sizes and two different operational scenarios “with” and “without” DR application are compared.

Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction, This work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project BENEFICE–PTDC/EEI-EEE/29070/2017 and UIDB/00760/2020 under CEECIND/02814/2017 grant

Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects., European Commission, Junta de Comunidades de Castilla-La Mancha, Agencia Estatal de Investigación

Energy usage and thermal comfort are the pillars of smart buildings. Many research works have been proposed to save energy while maintaining a comfortable thermal condition. However, most of them either make the over-simplified assumption on thermal comfort with unsatisfied comfort performance or deal with the single-zone thermal control only with limited practical impact. A few preliminary pieces of research on multi-zone control are available, but they fail to keep pace with the latest advancements in the deep learning-based control techniques. In this paper, we investigate the multi-zone thermal control with optimized energy usage and canonical thermal comfort modeling. We adopt the emerging multi-agent deep reinforcement learning techniques and propose to model each zone as an agent. A multi-agent framework is established to support the information exchange among the agents and enable intelligent thermal control in the heterogeneous zones. Accordingly, we mathematically formulate a problem to optimize both energy and comfort. A multi- zone thermal control algorithm (MOCA) is proposed to solve the problem by deriving optimal control policies. We validate the performance of MOCA through simulation in professional TRNSYS, configured based on our real-world laboratory. The results are promising with up to 15.4% energy-saving as well as satisfied thermal comfort in different zones. National Research Foundation (NRF) Accepted version This research is funded by National Research Foundation (NRF) via the Green Buildings Innovation Cluster (Grant NO.: NRF2015ENC_GBICRD001-012), administered by Building and Construction Authority (BCA) Singapore. In addition, this research is sponsored by National Research Foundation (NRF) via the Behavioural Studies in Energy, Water, Waste and Transportation Sectors (Grant NO.: BSEWWT2017 2 06), administered by National University of Singapore (NUS). Moreover, this research is funded by Nanyang Technological University (NTU) via the Data Science & Artificial Intelligence Research Centre @ NTU (Grant NO.: DSAIR@NTU).

Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1-min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM-H and ASY-H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013-2020., Junta de Comunidades de Castilla-La Mancha, Ministerio de Ciencia e Innovación, Ministerio de Economía y Competitividad

The understanding of the smart buildings in urban spaces covers various aspects of the smart buildings in relation to the urban spaces. This chapter introduces four aspects that are strongly interconnected: the software and hardware of the smart building systems, building types in the urban spaces, permits and standards for smart buildings, and smart building market potentials. A case study of the smart buildings in the Philippines is discussed in each section to provide an overview and understanding. The understanding of the smart buildings in urban spaces covers various aspects of the smart buildings in relation to the urban spaces. This chapter introduces four aspects that are strongly interconnected: the software and hardware of the smart building systems, building types in the urban spaces, permits and standards for smart buildings, and smart building market potentials. A case study of the smart buildings in the Philippines is discussed in each section to provide an overview and understanding.

Energy efficiency is a key to reduced carbon footprint, savings on energy bills, and sustainability for future generations. For instance, in hot climate countries such as Qatar, buildings are high energy consumers due to air conditioning that resulted from high temperatures and humidity. Optimizing the building energy management system will reduce unnecessary energy consumptions, improve indoor environmental conditions, maximize building occupant&rsquo, s comfort, and limit building greenhouse gas emissions. However, lowering energy consumption cannot be done despite the occupants&rsquo, comfort. Solutions must take into account these tradeoffs. Conventional Building Energy Management methods suffer from a high dimensional and complex control environment. In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. In this work, a Deep Reinforcement Learning agent is proposed for controlling and optimizing a school building&rsquo, s energy consumption. It is designed to search for optimal policies to minimize energy consumption, maintain thermal comfort, and reduce indoor contaminant levels in a challenging 21-zone environment. First, the agent is trained with the baseline in a supervised learning framework. After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. The performance is evaluated on a school model simulated environment considering thermal comfort, CO2 levels, and energy consumption. The proposed methodology can achieve a 21% reduction in energy consumption, a 44% better thermal comfort, and healthier CO2 concentrations over a one-year simulation, with reduced training time thanks to the integration of the behavior cloning learning technique.

The need for algorithms that optimize building energy consumption is usually motivated with the high energy consumption of buildings on a global scale. However, the current practice for evaluating the performance of such algorithms does not reflect this goal, as in most cases the performance is reported for one specific simulated building only, which provides no indication about the generalization of the score on other buildings. One approach to overcome this severe issue is to establish a shared collection of environments, each representing one simulated building setup, that would enable researchers to systematically compare and contrast the efficacy of their building optimization algorithms at scale. However, this requires that the individual environments are well designed for this goal. This paper is thus targeting the design of suitable environments for such a collection based on a detailed analysis of related publications that allows the identification of relevant characteristics for suitable environments. Based on this analysis a guide is developed that distills these characteristics into questions, intended to support a discussion of relevant topics during the design of such environments. Additional explanations and examples are provided for each question to make the guide more comprehensible. Finally, it is demonstrated how the guide can be applied, by utilizing it for the design of a novel environment, which represents an office building in tropical climate. This environment is released open source alongside this publication. We also indicate how test scenarios from existing publications could be enhanced to comply with the required characteristics according to our guide, underlining its importance for the future development and evaluation of building energy optimization algorithms, and thus for the sustainability of buildings in general.

Smart buildings and smart cities are not the future perspectives anymore&mdash, the smart building integration into a smart city is an actual question for today and tomorrow. Development of smart buildings not only enhances the smart city concept but also promotes positivity to the urban development and national economy, and increases the quality of life of the whole population reacting to global challenges of sustainability. The innovative smart building and smart city technologies enable us to overcome these challenges by being employed through all real estate (RE) project development stages. The Evaluation Framework for Real Estate Development in Smart Cities created by the authors provides the possibility to assess the existing as well as to forecast future RE projects integration into a smart city during the whole life-cycle stage. The practical application of the presented evaluation framework was illustrated by the comparative case study. Based on the created smart building integration into a smart city evaluation framework for real estate development, 10 RE projects in Lithuania and over the world were assessed and rated by selected criteria relevant to different RE development stages. The evaluation results revealed that, especially at the design and construction stages, the existing intelligence of RE projects and/or cities is insufficient. Although real estate projects are technologically advanced as single entities, the integration into smart city networks is limited by interoperability capabilities of the cities or by different strategic goals settled by real estate developers.

The COVID-19 pandemic, with more than 49.7 million reported cases and over 1.2 million deaths globally confirmed deaths at the time of writing, demands global action to counteract this virus. It is widely accepted that COVID-19 is a long-term pandemic that will require a constant and innovative range of mitigation approaches to protect public health. This paper provides infrastructure facility management (FM) systems based on Building Information Modeling (BIM) to reduce the likelihood of COVID-19 infections indoors. Although there are several factors for dealing with COVID-19, the sole focus of this project is to reduce crowding and facilitate social distancing between occupants. The significance of this research relies on the use of mathematical methods, BIM, programming as well as FM tools and databases to achieve safer management of large and populated public buildings during the COVID-19 pandemic. The infrastructure management example refers to the Civil Engineering School at the Universidad Polit&eacute, cnica de Madrid. It is based on mathematical applications to find the paths of people paths inside the infrastructure and is synchronized with in-house developed software and the Internet domain as source and input data.

Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.

This article presents a method to create a digital twin for sauna. The digital twin deploys a data analytics method based on fuzzy similarity measure for data summarization to provide real-time actionable instructions. The intended outcome of the digital twin is enhanced safety and improved sauna experience. In this research, we collected the data through experimentation with a testbed and interviews. Our implemented digital twin simplifies the readings of nineteen sensors into a single graphical form for the user that illustrates the overall sauna conditions and assists the sauna manager with condition optimization. Results illustrated the effectiveness of the digital twin in achieving an enhanced sauna condition for the users and providing the sauna manager with a decision support system. The method of creating the digital twin described in this article has potential implications for the condition monitoring, predictive maintenance, and enhancing the safety and security of complex systems.

BIM (Building Information Modeling) is an object-oriented approach for the digital modeling of buildings and infrastructures, a centralized system for sharing technical information among all Stakeholders during the entire life cycle of the structure. BIM is at the same time a tool for planning, evaluation, monitoring and documentation; however, some constraints determine a potential gap between represented reality and actual reality. This gap is determined by the fact that the information within the system is updated manually by expert designers, losing substantial synchronization with the real system under analysis. The risk is also to have a so extensive dataset to make its update management complex for a human user. Currently, to achieve direct interaction between civil structures and digital modeling systems, the scientific community is carrying out researches for the application of digital twin concepts in the field of construction automation. In particular, studies about the integration between IoT sensor networks and BIM start to be explored. The project described in this paper aims to propose such integration with a specific focus on air quality parameters monitoring, leveraging on scalable approaches. The data updating system is based on a distributed IoT sensor network, designed and implemented using free and open-source software; the communication protocols are all based on open broker-based technologies, like MQTT, and the data dashboard aimed to visualize real-time data and historical data series are based on web applications over HTTP. The research project led to the creation of a sensorized and BIM-modeled hub which demonstrates the validity of the concept.

Although smart environments are a key component of the Internet of Things (IoT), it is also clear that billions connected doors, washing machines, ovens and others will ultimately raise security and privacy concerns. Early work in this area, as well as most of commercial solutions, has adopted a centralized client/server approach, neglecting the multitude of risks that are induced by an unfair control of the server side. This has made the adoption of a decentralized and trust-less framework quintessential to guarantee devices security. Nevertheless, decentralized proposals are hardly applicable due to costs, slowness and privacy issues. In this paper, we make the use of blockchain practical for smart environments by designing HyBloSE, a secure-by-design and lightweight blockchain-based framework, able to run on low-power devices without additional hardware. HyBloSE is built by using Delegated Proof of Authority and a Moving Window Blockchain. We evaluate HyBloSE through a network emulator and real experiments with different Raspberry Pi platforms. Results show that HyBloSE guarantees a higher security level in terms of resiliency to internal and external attacks compared to centralized solutions, with overhead below 0.38s per operation and less than $4 per month for unlimited operations. Furthermore, we show how Proof of Authority is more adapt then Proof of Work in IoT private scenarios.

Like other islands, Malta experiences great challenges to secure its energy supply and independence. Deep renovation of buildings to nearly zero energy and addressing “smart-readiness” are widely believed to contribute to solving such challenges, while meeting the exigencies of the 2018 European Union energy performance of buildings directive (EPBD). Nearly zero energy buildings benchmarks for residential buildings in Malta have been defined using established EPBD cost-optimal methodologies, however these guidelines detailing a one-step and one benchmark definition approach neglects peak loads, building-grid interaction requirements and energy storage. To counteract these inadequacies, this research proposes an innovative multi-criteria approach adapted from ISO 52000-1:2017 standard, which supports the new EPBD requirements for optimizing comfort and addressing energy poverty. This is carried out by first optimizing adaptive comfort in “free-running mode,” before switching to mechanical space heating and cooling. When implementing this approach on a case study of an existing 40-family social housing block undergoing deep renovation, it was found that the discomfort hours have been reduced drastically, while the peak demand for the remaining discomfort hours requiring mechanical heating and cooling has been halved. Despite such positive impact of passive measures, the research has quantitatively demonstrated that given Malta’s temperate climate, such measures have lower impact on the energy rating of the building, when compared to that achieved with active and renewable energy (RE) measures. Thus, the proposed multi-tier benchmarking approach ensures that each energy efficiency measure is appropriately weighted on its own merits, rather than lumping all measures under a single benchmark indicator. With regards to smartness indicators for load matching and grid interaction, a detailed analysis using system advisor model software demonstrated that battery energy storage systems have the capacity to match the RE supply to the demand, although this approach is still far away from being cost-optimal. The research concluded that RE incentives should therefore move away from feed-in tariffs and subsidize direct energy use, storage, and load matching given their high costs. Furthermore, the cost-optimal analysis should also quantify the costs of thermal discomfort, energy poverty and grid mismatch, to ensure a holistic approach to deep renovation of buildings., peer-reviewed

Smart Building and its applications have become one of the essential ways of delivering a better quality of life and environment nowadays. The smart construction of building use the technology to share information between application by automate various processes, such as lighting, heating, ventilation and air conditioning (HV AC), security and other systems. A smart building and supervisory control systems infrastructure uses wired, wireless, and smart sensor networks to collect data according to the needs and services of the business. This paper presents a review and a comparison between the various communications technologies commonly used in smart building. Choosing an appropriate technology is crucial in smart building in ensuring the automated process run well.

Smart cities and smart buildings must provide their customers with many services, including those associated with health, productivity, and energy efficiency, among others. Short-range wireless systems can provide all of these services, but the significant growth of wireless networks operating within a smart building (SB) can produce the phenomenon of spectrum shortages. Spectrum shortages could be resolved using Cognitive Radio (CR)-based systems to improve the efficiency of electromagnetic spectrum use by taking advantage of the reusable spectrum available in the building&rsquo, s interior. This study proposes a mechanism using two interference conditions to quickly estimate the minimum amount of effective spectrum availability (ESA) inside an SB. The results show that an SB contains ESA distributed across 36% to 98% of the building&rsquo, s area for reuse, as a function of the height of the building and of the distance from the base station (BS) of the primary system.

The aim of this work was to develop an optimal model for an energy management strategy in a real micro-grid, which involves a smart building, a photovoltaic system with storage, and a plug-in full electric vehicle. A controller based on a mathematical algorithm was the core of each strategy, which directly acted on a relay board managing the interconnection between the different elements comprising the micro-grid. The development of an optimization model involving binary variables required an efficient code that achieved solutions in a short time. The analyzed case-study corresponded to the solar energy research center (CIESOL) smart building, a bioclimatic building, that is located at the University of Almería (Spain), designated to research in renewable energies. Using the methodologies described in this work, the total cost of the smart building energy consumption was minimized by decreasing the power supplied from the grid, especially at peak hours. Highlighting the use of a simple model that provided better performance than the current state of the art methodologies. The optimal model for energy management strategy demonstrated the advantages of using classical optimization techniques to solve this specific optimization problem, compared to a rule-based controller. The linear modeling was capable of producing a simple algorithm with less code development and a reduction in the computational effort.

The Internet of things presents tremendous opportunities for the energy management and occupant comfort improvement in smart buildings by making data of environmental and equipment parameters more readily and continuously available. Long-range (LoRa) technology provides a comprehensive wireless solution for data acquisition and communication in smart buildings through its superior performance, such as the long-range transmission, low power consumption and strong penetration. Starting with two vital indicators (network transmission delay and packet loss rate), this study explored the coverage and transmission performances of LoRa in buildings in detail. We deployed three LoRa receiver nodes on the same floor and eight LoRa receiver nodes on different floors in a 16-story building, respectively, where data acquisition terminal was located in the center of the whole building. The communication performance of LoRa was evaluated by changing the send power, communication rate, payload length and position of the wireless module. In the current research, the metrics of LoRa were quantified to facilitate its practical application in smart buildings. To the best of our knowledge, this may be the first academic research evaluating RTT performance of LoRa via practical experiments.

Smart Buildings with connected lighting and sensors have become one of the first large-scale applications of the Internet of Things. However, existing efforts to make buildings smarter focus mainly on energy conservation and cutting costs. In this paper, we further address the beneficial effects of light upon humans. People nowadays spend more than 90% indoors and as such the indoor environment becomes paramount for people's health and wellbeing. We present a Human Centric Lighting solution that supports human health through the estimation of the biological effects of light. It exploits the possibility that IoT offers to monitor humans and their experience and to control internet-connected lights. Despite the existence of well-established and extensively tested models of the circadian mechanism, benefits of those models are not yet harvested in practical applications. We envision an application that controls the lighting system based on a suitable model that predicts the human response to light. Our work confirms that the statistical signal processing approach of a Particle Filter can account for sensor and model uncertainties. We show how the response of the biological clock to light depends largely on individual characteristics, such as the intrinsic characteristic duration of the day, and therefore we include such person characterization into our system design.

Due to the increased awareness of issues ranging from green initiatives, sustainability, and occupant well-being, buildings are becoming smarter, but with smart requirements come increasing complexity and monitoring, ultimately carried out by humans. Building heating ventilation and air-conditioning (HVAC) units are one of the major units that consume large percentages of a building&rsquo, s energy, for example through their involvement in space heating and cooling, the greatest energy consumption in buildings. By monitoring such components effectively, the entire energy demand in buildings can be substantially decreased. Due to the complex nature of building management systems (BMS), many simultaneous anomalous behaviour warnings are not manageable in a timely manner, thus, many energy related problems are left unmanaged, which causes unnecessary energy wastage and deteriorates equipment&rsquo, s lifespan. This study proposes a machine learning based multi-level automatic fault detection system (MLe-AFD) focusing on remote HVAC fan coil unit (FCU) behaviour analysis. The proposed method employs sequential two-stage clustering to identify the abnormal behaviour of FCU. The model&rsquo, s performance is validated by implementing well-known statistical measures and further cross-validated via expert building engineering knowledge. The method was experimented on a commercial building based in central London, U.K., as a case study and allows remotely identifying three types of FCU faults appropriately and informing building management staff proactively when they occur, this way, the energy expenditure can be further optimized.