Search

The main purpose of this study is to provide insight into the trend of AI-BIM integration, which has been studied by scholars around the world. To begin, a systematic review and bibliometric analysis was conducted to investigate English articles published between 2015 and 2022. This paper presents a systematic, scientometric, science mapping analysis through qualitative and quantitative evaluation and co-occurrence methods using VOSviewer, CiteSpace, and Gephi software. Conclusions indicate future research should concentrate on integrating AI and other smart systems with BIM to enhance digitalization and improve outcomes throughout the construction project life cycle. Based on the qualitative and quantitative evaluation of each scope (BIM and AI) and their status quo, this study suggests integrating the following domains with BIM to reduce complexity in the construction industry in the future: robotics, cloud systems, AIOT, digital twins, 4D printing, and block chain. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process. [ABSTRACT FROM AUTHOR]

Research efforts focused on Smart Building (SB) development have concentrated on the automation of resources within intelligent environments towards an enhanced experience for occupants. The process converts everyday manual activities into automatic actions, such as turning on lights upon entering a room, activating air conditioning on hot days, and switching off a television when there are no viewers. This article addresses a case study on context-aware monitoring conducted at the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) of the University of São Paulo. The focus is on a Fog layer of the IoT operating closer to sensors and reducing communication delays. Concepts of context-aware systems, multi-agent systems, ontology, and MQTT protocol were considered for the implementation of the intelligent system on-site. The programming languages used were Java and Python, leveraging libraries and frameworks dedicated to such technologies. A scalable system that does not compromise computational resource use and maintains responsiveness with low data exchange latency is proposed and tests checked the intelligent behavior of the laboratory under specific conditions using temperature, luminosity, and presence of a person as parameters. [ABSTRACT FROM AUTHOR]

Power management in several sectors poses the problem of conserving the consumed power while satisfying the imposed conditions It is considered as a proactive control and management of the organization's energy consumption to save use and reduce energy expenses. Therefore, there is an actual need to include smart energy management systems in buildings in order to reduce the consumed energy. In this work, a comparative analysis is presented to evaluate deep and machine-learning approaches in the context of intelligent models for conserving power in smart buildings. The deep learning model is structured by using Deep Neural Networks (DNN), while machine learning models are represented by Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Naive Bayes (NB). These models adopt three classes: full (power in full consumption), select (power in partial consumption), and shout down (no power consumption). Moreover, feature reduction methods of Boruta and Principal Component Analysis (PCA) are implemented to reduce the complexity of the models. The proposed models are trained and tested using a measured dataset for a building. Comparison results of the proposed models showed that the Random Forest attracts more attention regarding classification accuracy by 100% and a reasonable classification time of 1.23 seconds. The effectiveness of the comparative analysis which indicating the highest accuracy results for RF makes it as a suitable model to be implemented as an optimal one in real-time power management systems. [ABSTRACT FROM AUTHOR]

Buildings are responsible for 40% of energy consumption in Europe, and 75% of them are energy inefficient. In this context, the opportunities enabled by improved management and conservation of energy in buildings are huge. There is a clear need to accelerate and finance building renovation investments and leverage smart, energy-efficient technologies if the EU wants to reach climate neutrality by 2050. IoT components enable new possibilities for improving efficiency in Smart Buildings, both in commercial and residential spaces. However, the literature misses some contributions aimed at evaluating the investment in IoT technologies used for improving the energy efficiency of a building used for different purposes. Thus, the objective of the present study is to provide an assessment of the main costs and benefits stemming from IoT technologies installation through retrofitting intervention and evaluate the sustainability of the investment. Data to feed the model were retrieved from academic literature and secondary sources. The results show that the investment can be recovered in the medium-short term. In particular, buildings with high consumption rates are the ones that benefit the most from this solution. The present study contributes to the academic literature by providing a model that considers a mixed building and multiple technologies at the same time. It also provides useful insights to whoever is interested in the application of IoT technologies to make a building smart, enabling the comprehension of necessary investment and economic returns. [ABSTRACT FROM AUTHOR]

Copyright of DYNA: Energía y Sostenibilidad is the property of Publicaciones Dyna SL 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.)

This article shows a set of physical information fusion IoT systems that we designed for smart buildings. Its essence is a computer system that combines physical quantities in buildings with quantitative analysis and control. In the part of the Internet of Things, its mechanism is controlled by a monitoring system based on sensor networks and computer-based algorithms. Based on the design idea of the agent, we have realized human-machine interaction (HMI) and machine-machine interaction (MMI). Among them, HMI is realized through human-machine interaction, while MMI is realized through embedded computing, sensors, controllers, and execution. Device and wireless communication network. This article mainly focuses on the function of wireless sensor networks and MMI in environmental monitoring. This function plays a fundamental role in building security, environmental control, HVAC, and other smart building control systems. The article not only discusses various network applications and their implementation based on agent design but also demonstrates our collaborative information fusion strategy. This strategy can provide a stable incentive method for the system through collaborative information fusion when the sensor system is unstable in the physical measurements, thereby preventing system jitter and unstable response caused by uncertain disturbances and environmental factors. This article also gives the results of the system test. The results show that through the CPS interaction of HMI and MMI, the intelligent building IoT system can achieve comprehensive monitoring, thereby providing support and expansion for advanced automation management. [ABSTRACT FROM AUTHOR]

Smart building is an application of an automatic control system to a building. Where this system has been arranged using a neatly structured algorithm. Almost all parts or building components can be managed automatically. Therefore, it can also be called a Building Automation System or BAS. The working principle of this smart building is the integration of various components in the building. From these installed components, apart from being able to be set automatically, there is also communication between components. In general, the method used for smart buildings is to use sensors. The larger the building, of course, the higher the energy required. If the property management still manages it manually, there is a tendency for a factor to be missed This makes energy use more wasteful. In addition to lighting and HVAC, many components of buildings use electricity. Such as pumps, dispensers, computers, televisions, and so on. It should be off or not connected to electricity when the building is empty. [ABSTRACT FROM AUTHOR]

The world is witnessing a shifting toward smart cities and the use of technology in cities. There are many rating systems to measure the smart readiness of existing buildings and cities, but they are not suitable in the Egyptian context. However, Egypt promotes sustainable and smart development through many codes, guidelines and rating systems. This research aims to develop a tool to measure the smart readiness of existing buildings and cities to help decision-makers determine the level of intervention needed for a building or a city to be smart. An induction research approach is used to deduce the smart readiness indicators for existing buildings and cities. The research ends by proposing 'Checklists' for measuring the smart readiness of Egyptian existing buildings and cities stock. [ABSTRACT FROM AUTHOR]

Blockchain technology is evolving across the globe and is being looked upon as a definite part of the future. Blockchain is often associated with bitcoin and finance's domain. But over the last decade, this backend technology to bitcoin has spread its association in almost all domains that we can think of. Further to this, smart contracts are making the blockchain ecosystem better. Other evolving technologies like Internet-of-things, Industrial Internet-ofthings, Cyber physical systems are also making their onset on the global platform. Smart buildings link Internet-of-things connectivity, sensors and the cloud to remotely supervise and assure efficient heating-air conditioning, lighting and security systems etc to improve efficiency and overall sustainability. The global buildings sector over the next 40 years is expected to add 230 billion square meters of fresh construction, i.e., adding the equivalent of Paris every week. Thus integrating these technologies right at the onset, before they grow in isolation, is a coveted need today. This paper proposes a prototype to simulate architecture and discusses how blockchain enabled smart buildings can further expedite automation, security and transparency. For an apprehension purpose, the paper focuses on smart contracts enabled repairs and service in smart buildings. [ABSTRACT FROM AUTHOR]

A doorway counter, which detects a person underpass at a room entry/exit, may be the most accurate type of occupancy counters used in buildings. An occupancy counter, which uses a low-resolution IR-imager and Raspberry Pi board has been constructed. The imager provides only 8 x 8 pixels initial resolution, but it has been enhanced using two-dimensional interpolation. Due to the low absolute accuracy in temperature measurements, the imager is set to measure temperature difference between a target and background. Signal-to-noise ratio is also increased using discrete two-dimensional convolution filtering. The blob detection and tracking algorithm deduces the direction of an occupant and finally increments or decrements the counter. A heat signature varies between people and depends on person's clothing. An on-board server on Raspberry Pi distributes the data via Wi-Fi to any client device in the net. The complete system includes also wireless PIR-sensors. The low-resolution IR occupancy counter has been compared with counters based on different technologies. The benefits of a low-resolution IR-imager are privacy preservation, operation capability in total darkness, energy-efficient passive operation and a low price. [ABSTRACT FROM AUTHOR]

Nowadays, the design of smart buildings (micro smart grids) has become one of the major concerns of almost all countries. Relying on clean or renewable energies to power homes, institutions or even factories has become a reality and an inevitable future. However, the distribution of energy stored or obtained must be done in an intelligent way to best serve all the building entities taking into consideration several factors such as: their priorities, nature of activities, the energy required, the duration of daily activity. The Internet of Things (IoT) is one of the primary components for micro smart grids, indeed thanks to IoT we can not only do the tele-survey of the consumption, but especially back up the reports to a controller entity allowing adapt the energy policy of the entire building. As a result, when we talk about a smart building, we are talking about a distributed sensor architecture. The management of these sensors must be done centrally to facilitate: (1) the prediction of the energy to be demanded, (2) the application of new energy balancing policies, (3) and the promotion of a source of energy compared to another depending on the weather conditions. Software-Defined Network (SDN) is an approach to orchestrate from a central point called a controller a set of nodes or devices present in the network and can be perfectly matched to the points previously discussed. Our contribution is in the context of the micro-smart grid, our paper proposes a new architecture for the efficient management and intelligent distribution of electrical energy obtained from all sources of renewable energy by adopting an SDN approach and by exploiting IoT sensors. The architecture is called Micro Smart Grid by Software-Defined Network (MSGSDN). A new algorithm is modeled and developed allowing this intelligent sharing. [ABSTRACT FROM AUTHOR]

The impact of indoor environment quality (IEQ) on occupants' wellness and productivity is substantial. Simultaneously, due to buildings' significant role as one of the largest energy consumers, there is a pressing need for improved energy efficiency and intelligence in building design and operation. This paper introduces a novel indoor environmental wellness index (IEW-Index) as a real-time key performance indicator (KPI) derived from controllable factors associated with wellness. The main objective of this index is to develop a universally applicable metric for IEQ, incorporate real-time data and personalized settings, and facilitate the real-time optimization of building control systems. In addition, a novel framework based on a multi-agent system (MAS) for building control systems is introduced, encompassing a detailed design of components and communication protocols. The IEW-Index's effectiveness has been successfully demonstrated through three real-world case studies formulated using the developed MAS framework. The findings indicate a direction for creating a sustainable building automation system that is intelligent and autonomous. • A real-time performance metric based on indoor environmental wellness factors. • A universally applicable measure using real-time data and personalized settings. • A multi-agent system framework towards automation of building control systems. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) can successfully be integrated into smart models for energy prediction, but require large datasets for training. This investigation presents an innovative methodology for photovoltaic power generation forecasting with ANNs, when only a limited amount of real data is available, and has been tested and validated on a real-life photovoltaic installation. Feature selection identifies which meteorological features most impact photovoltaic power generation. A simulator, which accurately models a real photovoltaic installation, is used to create an artificial, but accurate and realistic, dataset of power generation large enough to effectively train and test different ANNs. These are then exploited on a portion of real, but limited, dataset of power generated by the real photovoltaic installation on which the simulator is modeled. Finally, different transfer learning techniques are used to tune the ANN models with the remaining portion of the real, but limited, dataset of photovoltaic power generation. • Innovative PV power generation forecasting with ANNs, for limited real data. • IoT sensors to collect limited real data, simulator to generate artificial data. • ANNs trained on artificial data and tested on real data. • Transfer learning used to tune the ANN models. • Methodology tested on a real-life PV installation in Turin, Italy. [ABSTRACT FROM AUTHOR]

Group activities are an activity carried out together at the same time and place. In the office/university environment, they play a role in energy consumption. For this reason, the smart building needs group activity recognition (GAR) systems for electrical device control. Several studies on the GAR have been carried out in the outdoor environment, such as exercising, walking, or running. However, indoor group activities that share of energy consumption are found still rare, such as in meetings, seminars, and classroom activities. This study proposes a GAR method in the buildings using multi-image based on Camera through the sitting position of people and formed from the face identified from the image and visualized into the mapping. The simulation was carried out based on references from the scenario of the meeting, seminar, and class activity in the S307-room, Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada. The Neural Network algorithm was used to the GAR. The performance was evaluated in various measures such as precision, accuracy, and recall. The result of the GAR accuracy of the learning phase was 93.33%. The results of the GAR accuracy of testing phase were 63% and the error of GAR were 37%, respectively. [ABSTRACT FROM AUTHOR]

The Architecture, Engineering and Construction (AEC) industry has not adopted digital transformation enthusiastically like other industries, for instance, manufacturing, aerospace or finance. Building Information Modeling (BIM) is an innovative technology that is considered as an opportunity for the AEC industry to move to the digital era and improve the collaboration among stakeholders by applying Information and Communication Technologies (ICT). BIM provides all required tools and automations to achieve end-to-end communication, data exchange and information sharing among collaborators. Accordingly, virtual 3D models, created by the BIM process, delivered as physical assets, monitored in real-time and managed using Building Management Systems (BMS), can adopt the Internet of Things (IoT) designs and services. However, the implementation of IoT in a highly modular environment with various moving parts and inter-dependencies between stakeholders leads to security concerns. Therefore, this paper proposes system design that employs the blockchain technology as a measure to secure and control the framework that involves integrated IoT and BIM technologies. Although this paper considers system design applied in a smart museum, the authors assume that design is generic and applicable in other building categories. For instance, this design can be implemented in the ongoing Alumni Hall renovation project at State University of New York College at Oneonta. [ABSTRACT FROM AUTHOR]

Predicting electricity consumption represents one of most important information for efficient energy management in smart buildings. It is mainly used for occupancy prediction and the development of optimized control approaches of building's appliances (e.g., lighting and heating/air conditioning systems). Recently, several approaches have been proposed for load profiling, prediction and forecasting. The work presented in this paper is towards the development of load forecasting approaches for being integrated for occupancy prediction and context-driven control of building's appliances. We mainly investigated the accuracy of various machine learning and statistical methods for forecasting energy consumption. An IoT and Big Data based platform was deployed for gathering near-real time data about electricity/load consumption. Recorded data were used to deploy predictive models using ARIMA, SARIMA, XGBoost, Random Forest (RF), and Long Short-Term Memory. Experiments have been conducted and results are reported to shed more light on the accuracy of these methods for load forecasting. [ABSTRACT FROM AUTHOR]

Understanding occupant activities in a building is essential for building management systems to provide occupants with comfort and intelligent indoor environment. However, current occupant activity recognition mainly focuses on individual activity. Group activity recognition indoors has gained little attention, but remains of paramount importance, such as working together, taking classes, and discussions. In this paper, we propose a framework for group activity detection and recognition (i.e., GADAR framework) using smartphone sensors and Bluetooth beacons data. This framework consists of the following four layers: user layer, data package layer, processing layer, and output layer. As individuals within the group show similarity in motion, audio, and proximity, such similarity values are calculated and clustered into groups using hierarchical clustering. The framework then extracts the role, motion, speaking and location features from the clustered groups to distinguish different group activities. Decision tree classifier was selected to recognize the group activity that the group is engaged in. An experiment was conducted to identify the following three common group activities: taking class, seminar, and discussion. The result shows that the proposed GADAR framework could provide more than 89% accuracy in group detection and 89% accuracy in recognizing group activity. • We identify four challenges in group activity recognition considering group properties. • A group activity detection and recognition framework with four layers was proposed. • A prototype system was developed based on the framework and tested in a real setting. • The system recognized class, discussion, and seminar activities with 89% accuracy. • Four future research directions were discussed based on the case study. [ABSTRACT FROM AUTHOR]

Nowadays, the design of smart buildings (micro smart grids) has become one of the major concerns of almost all countries. Relying on clean or renewable energies to power homes, institutions or even factories has become a reality and an inevitable future. However, the distribution of energy stored or obtained must be done in an intelligent way to best serve all the building entities taking into consideration several factors such as: their priorities, nature of activities, the energy required, the duration of daily activity. The Internet of Things (IoT) is one of the primary components for micro smart grids, indeed thanks to IoT we can not only do the tele-survey of the consumption, but especially back up the reports to a controller entity allowing adapt the energy policy of the entire building. As a result, when we talk about a smart building, we are talking about a distributed sensor architecture. The management of these sensors must be done centrally to facilitate: (1) the prediction of the energy to be demanded, (2) the application of new energy balancing policies, (3) and the promotion of a source of energy compared to another depending on the weather conditions. Software-Defined Network (SDN) is an approach to orchestrate from a central point called a controller a set of nodes or devices present in the network and can be perfectly matched to the points previously discussed. Our contribution is in the context of the micro-smart grid, our paper proposes a new architecture for the efficient management and intelligent distribution of electrical energy obtained from all sources of renewable energy by adopting an SDN approach and by exploiting IoT sensors. The architecture is called Micro Smart Grid by Software-Defined Network (MSGSDN). A new algorithm is modeled and developed allowing this intelligent sharing. [ABSTRACT FROM AUTHOR]

Abstract Context-awareness is an important research area. Many energy efficiency applications of ubiquitous computing need to access some related contexts to provide the best and adequate energy saving services at the right time and at the right place. One of the big challenges is the difficulty and the complexity to make systems identify and understand situations in the building business, especially in complex situations to provide adequate energy-saving services for each of which. This issue is divided into two parts: On one hand, the smart building and its environment must be modeled in a way that provides context-awareness. On the other hand is the how to effectively exploit this context-awareness to reduce energy consumption and increase the user comfort. In this paper, we propose an intelligent context-awareness Building Energy Management System (ICA-BEMS). ICA-BEMS uses hybrid energy-saving techniques based on a Smart Context-Awareness Management (Smart-CAM). Smart-CAM uses smart building ontology to organize smart building knowledge and utilizes a new context-awareness mechanism to provide contextual information. The former exploits the contextual information in reasoning to reduce the building energy consumption, promoting users behavioral change and maximizing the user's comfort to bring about a better energy efficiency policy. To evaluate our system, we have developed a smart building simulator (Open-SBS) which simulates smart building comportments and human behaviors. We have created various scenarios of daily life in the smart building simulator. We have tested our ICA-BEMS effect on energy consumption having a positive result. Energy consumption has been decreased by 40% of total energy consumption. [ABSTRACT FROM AUTHOR]

Abstract Smart buildings as human-cyber-physical systems (h-CPSs) are capable of providing intelligent services, such as indoor positioning, personalized lighting, demand-based heating ventilation and air-conditioning, and automatic fault detection and recovery, just to name a few. However, most buildings nowadays lack the basic components and infrastructure to support such services. The investment decision of intelligent system design and retrofit can be a daunting task, because it involves both hardware (sensors, actuators, servers) and software (operating systems, service algorithms), which have issues of compatibility, functionality constraints, and opportunities of co-design of synergy. This work proposes a user-oriented investment decision toolset aimed at handling the complexity of exploration in the large design space and to enhance cost-effectiveness, energy efficiency, and human-centric values. The toolset is demonstrated in a case study to retrofit a medium-sized building, where it is shown to propose a design that significantly lowers the overall investment cost while achieving user specifications. [ABSTRACT FROM AUTHOR]

Abstract Building control is a challenging task, not least because of complex building dynamics ad multiple control objectives that are often conflicting. To tackle this challenge, we explore an end-to-end deep reinforcement learning paradigm, which learns an optimal control strategy to reduce energy consumption and to enhance occupant comfort from the data of building-controller interactions. Because real-world control policies need to be interpretable and efficient in learning, this work makes the following key contributions: (1) we investigated a systematic approach to encode expert knowledge in reinforcement learning through "experience replay" and/or "expert policy guidance"; (2) we proposed to regulate the smoothness property of the neural network to penalize the erratic behavior, which is found to dramatically stabilize the learning process and lead to interpretable control laws; (3) we established a virtual testbed for building control by combining the state-of-the-art building energy simulator EnergyPlus with a python environment to provide a systematic evaluation and comparison platform, which will not only further our understanding of the strengths and weaknesses of existing building control algorithms, but also suggest directions for future research. We experimentally verified our proposed deep reinforcement learning paradigm on the virtual testbed in case studies, which demonstrated promising results. [ABSTRACT FROM AUTHOR]

Abstract In commercial buildings, lighting contributes to about 20% of the total energy consumption. Lighting controllers that integrate occupancy and luminosity sensors to improve energy efficiency have been proposed. However, they are often ineffective because they focus solely on energy consumption rather than providing comfort to the occupants. An ideal controller should adapt itself to the preferences of the occupant and the environmental conditions. In this article, we introduce LightLearn , an occupant centered controller (OCC) for lighting based on Reinforcement Learning (RL). We describe the theory and hardware implementation of LightLearn. Our experiment during eight weeks in five offices shows that LightLearn learns the individual occupant behaviors and indoor environmental conditions, and adapts its control parameters accordingly by determining personalized set-points. Participants reported that the overall lighting was slightly improved compared to prior lighting conditions. We compare LightLearn to schedule-based and occupancy-based control scenarios, and evaluate their performance with respect to total energy use, light-utilization-ratio, unmet comfort hours, as well as light-comfort-ratio, which we introduce in this paper. We show that only LightLearn balances successfully occupant comfort and energy consumption. The adaptive nature of LightLearn suggests that reinforcement learning based occupant centered control is a viable approach to mitigate the discrepancy between occupant comfort and the goals of building control. Graphical abstract Image 1 Highlights • LightLearn adapts to occupant preferences and environmental conditions. • Experimental demonstration of LightLearn in five offices over eight weeks. • LightLearn successfully balances energy consumption with occupant comfort. [ABSTRACT FROM AUTHOR]

This paper considers a unified approach for virtual sensing in smart buildings that utilizes equation-based modeling and optimization-based parameter estimation. A modeling method is introduced to describe relationships among existing sensory information and application-oriented parameters in a reusable manner. Measurement errors in existing sensors are also described in this equation-based model. When parameters to be estimated and existing sensors used for virtual sensing are identified, relevant equations are selected and combined as simultaneous equations. The parameter estimation method takes an optimization-based approach to cope with uncertainty in sensory information. Simultaneous equations corresponding to target sensing are used as a constraint in the optimization problem. In cases where many different sensors are utilized to increase accuracy, we may need to consider compromising contradictions among sensory information due to sensing errors. If the target building does not have enough sensors for the intended parameter estimation, we may need certain assumptions to determine the value. Such over- or under-defined situations are automatically detected and considered in our parameter estimation mechanism. For this purpose, we introduce a method, based on an idea inspired by randomized algorithms, for repeatedly solving an optimization problem with different weight factors in the objective function and for analyzing any fluctuations in estimated values. This is computationally inexpensive and can be applied to big real-world problems. We applied this method to building occupancy estimations for efficient air-conditioning control and to customer attribute analysis for an office building cafeteria during lunch time. [Display omitted] • A unified method for virtual sensing uses various types of sensors for higher accuracy. • Equation-based modeling enables sensor modeling in a reusable manner. • Optimization-based parameter estimation copes with uncertainty in sensory information. • Over- or under-defined situations are detected and considered in parameter estimation. • Occupant estimation and people flow analysis problems are successfully solved. [ABSTRACT FROM AUTHOR]

This proof-of-concept paper proposes a method that integrates occupants' feedback and personal data to enhance the performance of fault detection and diagnostic (FDD) technology in buildings. The proposed approach uses a smartwatch app called Cozie to collect occupants' subjective feedback on thermal comfort and air quality, and the data is transferred to a cloud database through a data stream. Similarly, an application programming interface is used to retrieve sensor readings from the building automation system. FDD rulesets then use the data to determine process variable errors and report alarms. The results of an experiment show that occupant input accurately prove/disproved alarms and allowed for a faster and more accurate FDD. The occupants' feedback also helped prompt rule-based FDD algorithms and support the alarms triggered. Two independent surveys were also conducted. According to the first survey, occupants tend not to report discomfort to building operators through conventional methods unless the indoor temperature is more than 3 °C warmer or cooler than their desired temperature. The proposed method helped occupants report discomfort issues earlier, and the percentage of occupants choosing 3 °C warmer or colder conditions decreased by 29%. The paper concludes that the integration of occupants' feedback and personal data can significantly enhance FDD performance and provide more comfortable environments for building occupants. • Occupants' silence does not always indicate comfort or satisfaction. • Occupants seldom report discomfort issues unless the temperature deviates by ±3 °C. • Participants' subjective feedback successfully validated false and true alarms of FDDs. • The complaint management and FDD services were combined into a single platform. [ABSTRACT FROM AUTHOR]

This paper presents an architecture for smart buildings based on common-sense reasoning using the IndoorGML standard. The main objective is the construction of a knowledge model that allows inferring information that is not explicitly defined in that model. The main challenges addressed are: 1) the modeling of the concepts presented by the standard in the knowledge base used, known as Scone; 2) the automation of the introduction of knowledge in Scone with the proposed model; and 3) the implementation of a service-oriented architecture that makes possible to introduce knowledge in a transparent way. The semantic knowledge we present here, which shows more advanced and flexible capabilities, will make it possible in the future to carry out reasonings that not only depend on the structure of a building, but on many other aspects of the IoT such as sensors or actuators that are difficult to contemplate if a common sense approach is not followed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Buildings consumed 40% of the world’s primary energy consumption. In hot climate countries, more than 50% of building’s energy was spent on the cooling system. Aimed to reduce building’s cooling load in a tropical country, this study compares building’s construction configuration for two types of air-conditioned rooms in a high-rise office building. The two types of air-conditioned rooms are: (a) air conditioned rooms operating during office hours and (b) IT rooms and data centre with 24hours cooling. Six scenarios were compared using four building’s configurations (baseline, adding insulation, adding phase change material (PCM), and adding both insulation and phase change material) to evaluate the most efficient construction and cooling settings for both room types. It was found that for air conditioned rooms operating during office hours, adding PCM with melting point 29°C and force night time ventilation is the most effective in reducing cooling load. Meanwhile, for a room with 24hours cooling load, adding insulation material is the most efficient. PCM was only fully effective when night time ventilation was introduced which allows the PCM to discharge. High thermal conductivity walls have the potential to create a mould problem in the building (due to differences in moisture pressure at night time between the internal and external of the building) which can be eradicated by using night time ventilation or adding insulation to the wall. PCM installation reduced the sensible cooling load but not the latent cooling load. Hence, mechanical cooling or dehumidifier is still required. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Information Science & Technology Association/Joho no Kagaku to Gijutsu is the property of Information Science & Technology Association 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.)

A smart controls system can control and optimize building services. This article aims to improve mosque energy efficiency. To ensure a comfortable environment, indoor temperatures, lighting performance, and illumination levels were monitored. DIAlux simulation software visualized the lighting for the indoor mosque environment. To assess the impact of smart occupancy sensors on total energy consumption, the DesignBuilder simulation tool was used. Mosque energy consumption can be significantly reduced. By replacing traditional lighting units inside mosques with LEDs, 9 % of the total energy could be saved. Utilizing a smart control plan with the mosque's energy index of 69 kWh/m2/yr could significantly reduce energy consumption. Since there are fewer prayers, except for Friday prayers and Ramadan, energy-regulating smart control systems are applicable during most prayer times. The study focuses on the mosque as a sustainable building and proposes a new concept for mosques to function as energy-efficient buildings in hot arid climate. [ABSTRACT FROM AUTHOR]

The market is a tool used to efficiently allocate resources. Energy markets have been used to allocate generation and transmission resources at the level of the transmission and distribution system, with significant innovation on these markets occurring since deregulation in the 1990s. The advent of the Smart Grid and Smart Building have enabled these innovations to be brought to the level of the retail electricity market, where even individual buildings will be able to adjust their consumption based on price signals from the market. This paper gives a review of the development of energy markets and the technologies of the Smart Grid and Smart Building that are enabling their participation in the market at the edge of the grid. The OpenADR communication protocol is examined as a means of communicating price information between the load and the utility. Finally, a hardware-in-the-loop Smart Building test setup is described. This test setup is used to compare the performance of baseline and price responsive controls, with a power reduction of 60% achieved during a period of peak consumption and grid congestion corresponding to a large price surge. [ABSTRACT FROM AUTHOR]

Communication and information management are among the key areas of management operations at every stage of a construction project life cycle. The use of innovative digital technologies and tools for conducting such operations may significantly improve the preparation and execution of a construction project and it enables the preparation of a collection of information required to efficiently use the structure. The aim of this article is to present the results of the latest research related to the application of digitization in the Facility Management based on a critical literature review of 2010 - 2016. The article investigates the potential of Building Information Modelling to integrate data for the building lifecycle, providing information and decision support to facility management and maintenance. The results of the implementation of digital techniques for the purpose of smart Facility Management were discussed on the basis of a case study of The Edge office building in Amsterdam. The article also suggests possible directions for further research related to digitization in property management and its impact on the process of implementing the concept of intelligent and sustainable construction engineering. [ABSTRACT FROM AUTHOR]

This paper presents a novel building automation system that is decentralized and flat-structured. Each zone of the building is fitted with a smart node. It is a smart agent, which collects, handles and sends out information to its neighbours. All the smart nodes form a network that can realize self-organization and self-recognition. All kinds of control strategies can be converted into series of decentralized computing processes carried on by the smart nodes. The principle and mechanism of this decentralized, flat-structured building automation system are described in detail, and two use cases including evacuation in emergency and control of parallel-connected pumps based on this new system are further introduced. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for monitoring and data analysis in buildings, leading the user in the design and implementation of smart environments able to collect information on spaces and the comfort perceived by their inhabitants. A systematic descriptive framework is defined to identify the most convenient strategies for monitoring and data interpretation, driven by the constraints and goals set by how data will be exploited. A wide body of field works has been analyzed and put in relation to the methodology, highlighting the difficulty to extract general guidelines with respect to the same goal and to reveal use case specificity, preventing the possibility to learn from previous solutions. We claim the need to overcome the specific features of each case study to elicit general knowledge, and thus the quest for a unified framework, useful in different scenarios. [ABSTRACT FROM AUTHOR]

Demand response (DR) operates on the last mile in the smart grid that connects the building energy system with utility grid. It enables a building to actively participate in the whole grid energy system. With DR, a building may make a profit by optimizing its energy consumption while helping to achieve the overall targets of energy efficiency and emission reduction for the whole energy system. The recent US Supreme Court ruling on FERC Order 745 has reaffirmed the critical role and functions of demand response. It is expected that demand response will soon become a basic function of smart buildings. In this paper, we summarize the background of demand response, investigate promising automation technology such as OpenADR for demand response applications in smart buildings, and present an OpenADR Interface prototype demonstration for building energy management systems. We demonstrate the realization and benefits of OpenADR integration with state of art building energy management systems, which connects the last mile between the smart grids with the smart buildings. [ABSTRACT FROM AUTHOR]

As we are nowadays experiencing a transition period for the energy demand, there is a clear movement of the European energy market towards a new field of efficiency including reliable and smart systems that will upgrade the improvement of Europe's economic and environmental health. To this end smart systems introduce innovative applications with multiple and interdisciplinary characteristics: safe integration of additional renewables, distribution to the network, efficient delivery systems and monitoring control through demand response in order to achieve zero energy targets. The integration of smart technologies requires a holistic approach that takes into account all aspects of sustainability. The implementation of highly efficient smart buildings is feasible through the integration of smart metering, renewable systems acting as generators/storage and energy management. The holistic system supports and fulfils demand load management and distribution network of future grids.. Moreover, the benefits of effective thermal and electrical storage are underlined as a crucial factor of smart systems and smart buildings. This paper highlights the principles of integrated design procedure and links the process with smart building technologies. Energy efficiency methodologies and innovative techniques applied at building level are presented. To this context current EU policy framework, trends and perspectives concerning integrated design as a supportive tool for zero energy concept are also provided. [ABSTRACT FROM AUTHOR]

The intelligent building, a major smart city research and development domain, has grown beyond the scope of automation, currently focusing on the occupant centered approaches and the ancillary services offered to the power grid. The Internet of Things (IoT) introduced in the recent years are enabling those prospects. However, due to numerous legacy automation systems already in the construction market, the IoT based intelligent building approach is frequently challenged. This paper assesses the opportunities along with the criticism for a fully IoT enabled and controllable intelligent building against the well established, legacy automation systems in a fair and transparent approach. In the second part, it proposes an interoperable intelligent building design for the creation of advanced building managements schemes, by integrating the assets of current automation tools and the emerging innovations. This unification and the progressive phasing out of legacy technologies, would catalyze the market of intelligent buildings in the context of smart cities by not limiting their potential solely on new constructions and by mitigating the investment if legacy systems are already installed. [ABSTRACT FROM AUTHOR]

Demand controlled ventilation (DCV) is an important opportunity to reduce energy requirement especially in presence of variable occupancy. An evaluation of the possible amount of the energy savings consequent this more flexible control strategy are here presented in a real application case. This refers to the case of an ancient building in Venice. A part of this building was recently transformed in a modern university library. By recording all the measured data from the supervisory system an analysis of the annual performance of the DCV system was carried on. The investigation has pointed out the possibility of remarkable energy savings without compromising internal comfort conditions. [ABSTRACT FROM AUTHOR]

Optimizing Building energy consumption is a key solution to reducing their environmental impact. In this context, Information Technology can be harnessed by deploying sensors inside buildings, to collect relevant data about both energy consumed and occupant behavior, since occupants influence building appliances, such as HVAC, lights, and hot water tanks. Predicting room occupancy can be a solution to heat/cool rooms for instance. But, as prediction models are not often accurate, we may face situations where HVAC is activated while the rooms are empty or vice-versa, leading to either a waste of energy or a lack of occupants' comfort. To predict user behavior, detect prediction errors, and correct the model, we introduce a graph mining-based optimization method that combines an occupant behavior prediction model and a selective reinforcement learning method, where error detection relies on sensors that detect real-time occupancy of rooms. We experimented with our approach on simulated data and results showed that, compared to conventional HVAC management, our model can reduce up to 57.8% of HVAC energy consumption, and provide up to 94.3% of occupants' comfort when using the prediction method only, and up to 80.1% of HVAC energy consumption, and provide up to 97% of occupants' comfort when using the reinforcement method to correct prediction errors. • Ensure occupants' thermal comfort while optimizing building energy consumption. • Graph Mining and Reinforcement Learning to optimize building energy usage. • Room occupancy status tracked based on a real-time data collected by sensors. • Occupancy tracking is used to correct the prediction errors. • Promising results obtained for HVAC energy consumption saving user's thermal comfort. [ABSTRACT FROM AUTHOR]

• An MSG with a solar cell, wind turbine, diesel generator, battery, and SG is modelled. • Smart buildings of different types and different loads are taken into account. • Fuzzy controllers are used for the management of the MSG and smart buildings. • Simulation results for smart buildings in connection with MSG and SG are provided. • Results show the superiority of the designed and optimized fuzzy controllers. This manuscript proposes an intelligent supply and demand management system in a complete network of electricity production and consumption. A micro smart grid (MSG), which includes a solar cell, a wind turbine, a diesel generator, and battery storage system capable of trading energy with the smart gride (SG), connected to smart buildings with different types of loads is modelled. Different types of intelligent fuzzy controllers for distributed management were proposed and optimized via the non-dominated sorting genetic algorithm-II (NSGAII), which is a multi-objective optimization method. Maximum user comfort, the amount of renewable energy employment, minimum total power consumption cost, total energy consumption at peak time, and MSG loss of power supply probability are the five objective functions of the optimization process. Various uncertainties of the real world have also been considered. The most crucial distinguishing feature of this proposed method is the design of controllers to manage the demand and supply of electricity without the need for daily optimization. Comparison experiments with other methods presented in the field of electricity supply and demand management are conducted to show the superiority of this method in terms of optimality of the results, low processing volume required to implement real controllers, and its resilience to changing conditions. [ABSTRACT FROM AUTHOR]

Building energy monitoring and real time control strategies can decrease energy consumption on one hand, and improve comfort on the other hand, by increasing the understanding of the control system. A decision support system for building energy management can be a proper tool for supporting the measurement and management of energy usage and costs of public buildings. The aim of the paper is to describe the architecture of the Decision Support System (DSS), that is being developed within the FP7-Smartcities Project OPTIMUS (OPTimising the energy Use in cities with smart decision support system). The architecture of the system is described considering both the energy related and the information technology aspects. An example of action modeling is also presented and the first results are discussed. [ABSTRACT FROM AUTHOR]

Occupancy information in indoor spaces is playing an increasingly important role in the development of smart applications. The need for this type of information covers a multitude of domains in the Smart Buildings paradigm such as improving energy saving or occupant comfort. For this reason, we can find many works in the literature focused on occupancy tracking/monitoring using solutions based on RGB cameras and computer vision techniques, sensors and machine learning techniques, or air quality control, among others. But these solutions have limitations. Some of them do not support the tracking of people between spaces, the time to update information is too long, or the system used is too intrusive. This paper presents a solution to estimate the occupancy level in indoor spaces of different areas through depth cameras. This approach also proposes the integration of neural networks to deal with situations where the data collected from the environment is incomplete, filling the gaps caused by occlusion or performance problems. Finally, an occupancy service has been designed and deployed in order to provide occupancy information to other applications, such as evacuation services. The experiments carried out show how it is possible to obtain an accuracy of 90.20% through this approximation. In addition, we face some of the limitations mentioned above: the solution allows tracking movement and occupancy in large spaces without (1) lighting dependencies and (2) the requirement for users to wear devices. This, and the high accuracy obtained make the proposed work a great alternative for occupancy estimation in indoor spaces. • A non-intrusive and low-cost approach for estimating occupancy information in indoor environments. • Smart services to provide occupancy information to applications aimed at improving building performance. • Identification of the origin and destination of trajectories captured by depth cameras. • Neural network for completing incomplete trajectories by point estimation. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for intelligent power utilization in smart building based on particle swarm optimization with adaptive chaotic inertia weights (AIWPSO) to minimize electric cost and maximize user's comfort. Suppose that photovoltaic (PV) generation, batteries, micro turbines, controllable loads and uncontrollable loads exist in smart building. The proposed AIWPSO algorithm used in solving the intelligent power utilization model can further improve the performance compared with PSO algorithm in terms of convergence speed and accuracy as well as the global searching ability because of the chaotic characteristics and adaptive nature provided by the success rate which can provide the state information of the swarm and help them adjust the inertia value for better position. The effectiveness of AIWPSO is evaluated and compared with the standard PSO, and the simulation results demonstrate the effectiveness of the proposed intelligent power utilization strategy, which can reduce electric cost and increase user's comfort through optimally operate power equipments and appliances according to self demand. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for intelligent power utilization in smart building based on particle swarm optimization with adaptive chaotic inertia weights (AIWPSO) to minimize electric cost and maximize user's comfort. Suppose that photovoltaic (PV) generation, batteries, micro turbines, controllable loads and uncontrollable loads exist in smart building. The proposed AIWPSO algorithm used in solving the intelligent power utilization model can further improve the performance compared with PSO algorithm in terms of convergence speed and accuracy as well as the global searching ability because of the chaotic characteristics and adaptive nature provided by the success rate which can provide the state information of the swarm and help them adjust the inertia value for better position. The effectiveness of AIWPSO is evaluated and compared with the standard PSO, and the simulation results demonstrate the effectiveness of the proposed intelligent power utilization strategy, which can reduce electric cost and increase user's comfort through optimally operate power equipments and appliances according to self demand. [ABSTRACT FROM AUTHOR]

“Smart” buildings is an emerging technology. “Smart” buildings differ from usual buildings, so designing process is different as well. Nowadays norms and standards as well as proved tools for designing “smart” buildings still don’t exist. Each building has its own, sometimes unique, functional purpose and characteristics. Thus, a “smart” building solution that proved its reliability for one building might fail for another. Designers want to know how to choose the best “smart” building solution for a particular building? This article proposes a simulation process for calculating Building Intelligence Quotient which can be used as a measure for choosing “smart” building solution. [ABSTRACT FROM AUTHOR]

“Smart” buildings is an emerging technology. Very often a building “intelligence” is confused with just a building automation. This paper explores distinctive features of “smart” buildings. It was figured out that a formal definition and a quantitative measure of a building “intelligence” do exist. However, due to non-deterministic nature of Artificial Intelligence, a simulation of building operations is required to calculate Building Intelligence Quotient. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) is a powerful technology that is used to support decision-making about a building during its life-cycle. This research explores application of BIM technology to “smart” buildings. “Smart” buildings differ from usual buildings. The biggest differences occur in the design and the operational stages. The article shows that traditional BIM solutions, due to its static nature, don’t cover all needs of “smart” buildings technology. Dynamic extension of Building Information Model is proposed to cover gaps of traditional BIM in the design and operational stages of “smart” buildings life-cycle. [ABSTRACT FROM AUTHOR]

This paper presents a novel real-world application for energy savings in a Smart Building environment. The proposed system unifies heterogeneous wireless sensor networks under a Semantic Web Service middleware. Two complementary and mutually exclusive rule-based approaches for enforcing energy-saving policies are proposed: a reactive agent based on production rules and a deliberative agent based on defeasible logic. The system was deployed at a Greek University, showing promising experimental results (at least 4% daily savings). Although the percentage of energy savings may seem low, the greatest merit of the method is ensuring that no energy is wasted by constantly enforcing the policies. [ABSTRACT FROM AUTHOR]

Today, a new generation of software applications can be developed, as IT services developed by software providers can be integrated by solution developers in complex control and monitoring Smart Building Management solutions. This paper introduces the concept of Virtual Smart Building Service Enterprise (SBSE) as a novel perspective to develop an innovated services ecosystem for smart buildings. The Virtual SBSE allows different stakeholder categories to interact in order to develop, publish, compose, discover, integrate and use specific functionalities exposed as services for smart building management. An extended implementation case study is developed around an open software platform for modelling, virtualising, and managing complex services, including energy consumption. It integrates in the operational loop a Smart Building Controller exposed as a service that home users can download, install and use in their homes for the control and monitoring of energy consumption, or for ambient comfort. Different schedules and policies for individual devices or groups of devices can be defined based on daily working or living user schedules, so that the users may evaluate and adjust energy cost based on their own preferences, achieving their energy efficiency goals. The Virtual SBSE concept and the energy efficiency case study presented in the paper demonstrate how the easy open integration of diverse information systems enabling the creation of new smart building management services and involving users in value co creation can be attained. [ABSTRACT FROM AUTHOR]

It is envisioned that other than the grid-building communication, the smart buildings could potentially treat connected neighborhood buildings as a local buffer thus forming a local area energy network through the smart grid. As the hardware technology is in place, what is needed is an intelligent algorithm that coordinates a cluster of buildings to obtain Pareto decisions on short time scale operations. Research has proposed a memetic algorithm (MA) based framework for building cluster operation decisions and it demonstrated the framework is capable of deriving the Pareto solutions on an 8-h operation horizon and reducing overall energy costs. While successful, the memetic algorithm is computational expensive which limits its application to building operation decisions on an hourly time scale. To address this challenge, we propose a particle swarm framework, termed augmented multi-objective particle swarm optimization (AMOPSO). The performance of the proposed AMOPSO in terms of solution quality and convergence speed is improved via the fusion of multiple search methods. Extensive experiments are conducted to compare the proposed AMOPSO with nine multi-objective PSO algorithms (MOPSOs) and multi-objective evolutionary algorithms (MOEAs) collected from the literature. Results demonstrate that AMOPSO outperforms the nine state-of-the-art MOPSOs and MOEAs in terms of epsilon, spread, and hypervolume indicator. A building cluster case is then studied to show that the AMOPSO based decision framework is able to make hourly based operation decisions which could significantly improve energy efficiency and achieve more energy cost savings for the smart buildings. [ABSTRACT FROM AUTHOR]