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A wireless networked control system (WNCS) consists of a dynamic system to be controlled, sensors, actuators, and a remote controller. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In this paper, we are surveying the literature on the communication networks in WNCSs and the challenges related to them, such as the communication standards, delay, Packet dropout, and delay jitter. Then, the control approaches in the design of a WNCS are presented, including the interactive design approaches and the joint design approaches. Also, several applications of WNCSs have been discussed in terms of their structure, functionality, and control design. These applications include Intra-Vehicle Wireless networks, Wireless Avionics Intra-Communication, Building Automation, and Water pumping. After that, security issues in WNCSs from a control engineering point of view are detailed while focusing on the major kinds of cyber attacks affecting WNCSs. Finally, future directions and conclusions are summarized at the end of the paper. [ABSTRACT FROM AUTHOR]

In this article we aim to make an analysis of the evolution of security systems for smart homes and to present some effective solutions. Manufacturers are not developing smart devices from scratch, but are using existing solutions launched by large IT companies to help build smart home networks. The sensors are integrated in different places in homes, offices, equipment, sometimes even clothes, to detect and monitor the location, actions, necessary signs, room temperature and humidity levels of users properties. However, ensuring confidentiality and effective security is a major challenge for smart homes. The increased risk of data security and information leakage makes smart home security an important challenge that needs to be carefully considered. [ABSTRACT FROM AUTHOR]

With the increasing deployment of smart buildings and infrastructure, supervisory control and data acquisition (SCADA) devices and the underlying IT network have become essential elements for the proper operations of these highly complex systems. Of course, with the increase in automation and the proliferation of SCADA devices, a corresponding increase in surface area of attack on critical infrastructure has increased. Understanding device behaviors in terms of known and understood or potentially qualified activities vs unknown and potentially nefarious activities in near-real time is a key component of any security solution. In this paper, we investigate the challenges with building robust machine learning models to identify unknowns purely from network traffic both inside and outside firewalls, starting with missing or inconsistent labels across sites, feature engineering and learning, temporal dependencies and analysis, and training data quality (including small sample sizes) for both shallow and deep learning methods. To demonstrate these challenges and the capabilities we have developed, we focus on Building Automation and Control networks (BACnet) from a private commercial building system. Our results show that "Model Zoo" built from binary classifiers based on each device or behavior combined with an ensemble classifier integrating information from all classifiers provides a reliable methodology to identify unknown devices as well as determining specific known devices when the device type is in the training set. The capability of the Model Zoo framework is shown to be directly linked to feature engineering and learning, and the dependency of the feature selection varies depending on both the binary and ensemble classifiers as well. [ABSTRACT FROM AUTHOR]

Wireless networked control systems (WNCS) are composed of spatially distributed sensors, actuators, and controllers communicating through wireless networks instead of conventional point-to-point wired connections. While WNCSs have a tremendous potential to improve the efficiency of many critical control systems, for instance, in building automation and process control, the systems are fundamentally constrained by the packet losses and the functional faults of the underlying wireless sensor and actuator networks. Understanding the interaction between wireless networks and control systems is essential to characterize the performance limitations of the critical control systems and optimize its wireless network resources. This paper presents an analytical framework for modeling the behavior of the control loop over lossy and faulty network. The control loop over wireless networks is modeled through a Markov chain taking into account sensing links, actuating links, and recovery mechanism to compensate the faulty nodes. By using this model, the novel performance metrics are mathematically derived and are evaluated through both theoretical analysis and simulation results. The performance evaluation shows the critical tradeoff between the average performance when the control loop is in the normal operation mode and the recovery performance when it is in the abnormal operating mode due to the faulty nodes. [ABSTRACT FROM AUTHOR]

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics. [ABSTRACT FROM AUTHOR]

Abstract Most existing and new buildings adapt poorly to variable occupancy, in part due to technical constraints and common operational practice. Although building automation systems and advanced control strategies aim to address this issue by improving adaptability to partial occupancy, no holistic metrics exist to quantify this aspect of building performance. To this end, we present a technology-independent approach to define adaptability as a building performance attribute and introduce metrics to quantify it. These metrics can be used to evaluate how different building technologies and control strategies influence building operations' adaptability to variable occupancy or estimate their associated energy savings. To demonstrate these metrics, a case study based on simulating a single-story office building was used to compare several control strategies with regards to their effect on improving adaptability. Results showed how the proposed metrics highlighted the additional benefits of these control strategies, especially under low-occupancy scenarios. Performance-based compliance with building energy codes and standards typically assumes full or near full occupancy, which may underestimate the benefits of adaptable building technologies or controls. Therefore, incorporating adaptability metrics in energy codes and operational guidelines would quantify the benefits of adaptable systems, especially under variable occupancy. Highlights • A new set of performance metrics to quantify buildings' adaptability are proposed. • Adaptability is defined as the degree to which energy use is proportional to occupancy. • The metrics are used to evaluate building operations under variable occupancy scenarios. • Contrary to energy-centric metrics, they highlight the benefits of adaptable controls. [ABSTRACT FROM AUTHOR]

Abstract In recent years, technological advances have substantially extended the capabilities of building automation. Despite the achieved advances, evidently, automation have not been widely adopted by occupants in buildings. To enhance automation adoptability, automation procedure in buildings should involve determination of user's preferred automation levels, in different conditions and control contexts and learning preference dynamics in time. In line with this motivation, in this paper we introduce a building automation that learns occupant's preferences continuously to fully or partially control the service systems in buildings based on a set of dynamic rules that are generated with the insight about user's preferences and activities. The algorithmic components of our proposed automation include (1) dynamic command planning, (2) adaptive local learning, and (3) iterative global learning. In order to evaluate these algorithms, we used a combination of real and synthetic user activity and preference data from an office with five occupants and an apartment with one occupant. Based on our results from evaluation of adaptive local learning, after a certain number of days (i.e., 8.5 days in average) the accuracy of predicting participants' preference reached to an acceptable value (i.e., above 85%). About 24% to 75%, 5% to 45%, and 6% to 49% of the total daily energy consumption of the participants could be saved using full automation, adaptive automation and inquisitive automation, respectively. Our results for evaluating iterative global learning algorithm showed that adaptive automation has the highest sum of the rewards from achieved benefit and user satisfaction and inquisitive automation has the second highest reward values. Full automation and no automation came in third and last spots, respectively. [ABSTRACT FROM AUTHOR]

Highlights • A scalable and smart HVAC control approach. • An IoT architecture and prototype for implementing the control. • Deployment results in a test-bed. • Implementation aspects with IoT and legacy BMS. Abstract Scalability of control algorithms used for savings energy in commercial building Heating, Ventilation and Air-Conditioning (HVAC) system and their implementation on low cost resource constrained hardware is a challenging problem. This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software, networking, and their integration) along with their interactions. A detailed description of the different components, hardware design, deployment issues, and their integration with legacy systems as well as cloud-connectivity is presented. In addition, simple modifications required for transforming the optimization models to an active control technique is also presented. While scalability is provided by the decentralized control, recursive zone thermal model identification, prediction occupant's thermal sensation, and embedding them within the optimization models enhances the smartness. Consequently, due to the implementation of Smart-TBSA using IoT devices, an otherwise centralized control architecture of the legacy building automation system is transformed to a more scalable and smart decentralized one. The proposed Smart-TBSA and IoT prototype are illustrated on a pilot building in Nanyang Technological University, Singapore having 85 zones. Our results shows that by combining IoT with decentralized control, energy savings up to 20% can be derived. Moreover, we show that legacy building automation system can be transformed into a more smart, adaptable, scalable, and decentralized control by deploying IoT devices without incurring significant costs. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks, cloud computing, and the Internet of Things are among the emerging technology enablers offering a wide array of new application possibilities in smart urban spaces. These applications consist of smart building automation systems, healthcare monitoring systems, and intelligent and connected transportation, among others. The integration of IoT-based vehicular technologies will enrich services that are eventually going to ignite the proliferation of exciting and even more advanced technological marvels. However, depending on different requirements and design models for networking and architecture, such integration needs the development of newer communication architectures and frameworks. This work proposes a novel framework for architectural and communication design to effectively integrate vehicular networking clouds with IoT, referred to as VCoT, to materialize new applications that provision various IoT services through vehicular clouds. In this article, we particularly put emphasis on smart city applications deployed, operated, and controlled through LoRaWAN-based vehicular networks. LoraWAN, being a new technology, provides efficient and long-range communication possibilities. The article also discusses possible research issues in such an integration including data aggregation, security, privacy, data quality, and network coverage. These issues must be addressed in order to realize the VCoT paradigm deployment, and to provide insights for investors and key stakeholders in VCoT service provisioning. The article presents deep insights for different real-world application scenarios (i.e., smart homes, intelligent traffic light, and smart city) using VCoT for general control and automation along with their associated challenges. It also presents initial insights, through preliminary results, regarding data and resource management in IoT-based resource constrained environments through vehicular clouds. [ABSTRACT FROM AUTHOR]

Abstract This work introduces a new technique that provides real-time feedback to a Heating, Ventilation, and Air Conditioning (HVAC) system controller with respect to the occupants' thermal preferences to avoid space overheating. We propose a non-invasive approach for automatic prediction of personal thermal comfort and mean time to warm discomfort using machine learning. The prediction framework described uses temperature information extracted from multiple local body parts to model an individual's thermal preference, with sensing measurements that capture local body part variance as well as differences between body parts. We compared the efficacy of using machine learning with classical measurements such as skin temperature along with our approach of using multi-part measurements and derived data. An analysis of the performance of machine learning shows that our method improved the accuracy of personal thermal comfort prediction by an average of 60%, and the accuracy of mean time to warm discomfort prediction by an average of 40%. The proposed thermal models were tested on subjects' data extracted from an office setup with room temperature varying from low (21.11 °C) to high (27.78 °C). When all proposed features were used, personal thermal comfort was predicted with an accuracy higher than 80% and mean time to warm discomfort with more than 85% accuracy. Further analysis of the machine learning efficacy showed that temperature differences had the highest impact on performance of individual thermal preference prediction, while the proposed approach was found not sensitive to the actual machine learning algorithm. Highlights • Machine learning method for non-invasive individual thermal preference prediction with more than 80% accuracy. • Temperature differences improve the most individual thermal preference prediction. • Face temperature variance improves personal thermal comfort prediction. • Proposed model is not sensitive to the actual machine learning algorithm. • Signal needed for thermal comfort discrimination is spread across multiple features. [ABSTRACT FROM AUTHOR]

Buildings with mixed residential and commercial units show relevant power peak that are further increased by shifting from gas-driven systems to an electric source. The proposed solution is to organize a microgrid for such type of buildings, aggregating different users with a common electric distribution system with a single connection to the grid, a local common generation, and a common heating/cooling system (electric driven). This approach upgrades a group of independent small users with rigid loads and chaotic behavior to a large user with a flexible and controlled profile. A central building automation control system managing all built-in technical systems and smart appliances may control the load, minute by minute, shifting in time shiftable and controllable loads, and merging different kinds of loads, obtaining a flatter diagram. The authors consider the suggested approach convenient to realize a demand side management (DSM) for residential/commercial buildings. distribution system operator (DSM) exploits the flexibility of smart appliances and the thermal inertia of the structure, by imposing local and central set points of heating and cooling systems, according to actual global net load and generation at a given moment. In this paper, main aspects of the proposed control system are presented and simulations for a given case study with a local photovoltaic system generation are provided. Results show that this approach may lead to a power peak reduction up to 20% even in the unfavorable case of combining commercial and residential units. Moreover, full self-consumption of locally generated energy from renewable energy systems may be achieved. [ABSTRACT FROM AUTHOR]

According to the U.S. Department of Energy, a significant portion of energy used in buildings is wasted. If the occupancy quantity in a pre-determined thermal zone is aware, a building automation system (BAS) is able to intelligently adjust the building operation to provide "just-enough" heating, cooling, and ventilation capacities to building users. Therefore, an occupancy counting device that can be widely deployed at low prices with low failure rate, small form-factor, good usability, and conserved user privacy is highly desirable. Existing occupancy detection or recognition sensors (e.g., passive infrared, camera, acoustic, RFID, CO2) cannot meet all these above system requirements. In this work, we present an IoT (Internet of Things) prototype that collects room occupancy information to assist in the operation of energy-efficient buildings. The proposed IoT prototype consists of Lattice iCE40-HX1K stick FPGA boards and Raspberry Pi modules. Two pairs of our prototypes are installed at a door frame. When a person walks through this door frame, blocking of active infrared streams between both pairs of IoT prototypes is detected. The direction of human movement is obtained through comparing occurrence time instances of two obstructive events. Thus, the change in occupancy quantity of a thermal zone is calculated and updated. Besides, an open-source application user interface is developed to allow anonymous users or building automation systems to easily acquire room occupancy information. We carry out a three-month random test of human entry and exit of a thermal zone, and find that the occupancy counting accuracy is 97%. The proposed design is completely made of off-the-shelf electronic components and the estimated cost is less than $160. To investigate the impact on building energy savings, we conduct a building energy simulation using EnergyPlus and find the payback period is approximately 4 months. In summary, the proposed design is miniature, non-intrusive, ease of use, low failure rate, and cost-effective for smart buildings. [ABSTRACT FROM AUTHOR]

Abstract Building Energy Management is identified as a key part of the move towards localised energy generation and control. The significant discrepancy between building energy use as designed and during actual operation shows a need to evaluate the relationship between building occupants and energy requirements. The need to better account for the influence of occupants on building energy use has been established through post-occupancy studies, highlighting the characteristics needed for more successful building control systems. This paper provides an overview of current building control systems technology and discusses existing academic research into more advanced occupant-centric controls. The potential for application of various methods is compared. It is found that study into occupant-centred control systems covers a wide array of approaches, ranging from simple presence-based switching of lighting systems to full model predictive control. Studies suggest an optimum point balancing the complexity of a system against its potential for saving energy. Highlights • Occupant-centred Building Energy Management technologies and systems were reviewed. • Occupant measurement technologies varied in accuracy across different building types. • Wider occupancy data increasingly feasible with smart devices and wireless sensors. • Studies suggest an optimum, balancing system complexity against potential for saving energy. • A major challenge is robustness to uncertainty around unexpected occupant behaviours. [ABSTRACT FROM AUTHOR]

Highlights • Application Methodology of adaptive control algorithms in buildings. • Implementation in HVAC system of the results of a field study has been carried out in buildings in Seville. • Software implementation for Building Automation System to manage the thermal comfort. • Operating air conditioning in a mixed mode strategy has significant energy benefits. Abstract Although there are many fieldstudies to achieve a model of comfort in free running buildings, fewer studies focus on mixed-mode buildings. Moreover, there are even fewer examples of implementing such algorithms into a building automation system for testing its real validity. In this study, a methodology for implementing and validating an Adaptive Control Algorithm in mixed mode buildings is proposed. In particular, the paper shows the implantation and application of an experimental adaptive control algorithm in the current installation of an office building and without additional costs or specific hardware. The experiment seeks to find a relationship between comfort of their occupants and with energy efficiency. The implementation into the building´s system shows the real applicability and the effectiveness of the adaptive model to hybrid buildings, highlighting that the methodology proposed could be applied in another type of building. The results show that it is possible to improve the energy efficiency, while maintaining the comfort of the users using only the tools yet available in the Building Automation System of the buildings and without additional systems, no extra costs and minimum intervention in its control system. [ABSTRACT FROM AUTHOR]

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish 'occluded' from 'empty' regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same plane. [ABSTRACT FROM AUTHOR]

The knowledge about the occupancy of an indoor space can serve to various domains ranging from emergency response to energy efficiency in buildings. The literature in the field presents various methods for occupancy detection. Data gathered for occupancy detection, can also be used to predict the number of occupants at a certain indoor space and time. The aim of this research was to determine the number of occupants in an indoor space, through the utilisation of information acquired from a set of sensors and machine learning techniques. The sensor types used in this research was a sound level sensor, temperature/humidity level sensor and an air quality level sensor. Based on data acquired from these sensors six automatic classification techniques are employed and tested with the aim of automatically detecting the number of occupants in an indoor space by making use of multi-sensor information. The results of the tests demonstrated that machine learning techniques can be used as a tool for prediction of number of occupants in an indoor space. [ABSTRACT FROM AUTHOR]

Building automation systems provide abundant sensor data to enable the potential of using data analytics to, among other things, improve the energy efficiency of the building. However, deployment of these applications for buildings, such as, fault detection and diagnosis (FDD) on multiple buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their location, function, etc.), which is required by applications. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. To better understand the nature of the problem, as well as the performance and scalability of existing solutions, we implement and test 6 different time-series based metadata inference approaches on sensors from 614 air handling units (AHU) instrumented in 35 building sites accounting for more than 400 buildings distributed across United States of America. We infer 12 types of sensors and actuators in AHUs required by a rule-based FDD application: AHU performance and assessment rules (APAR). Our results show that: (1) the average performance of these approaches in terms of accuracy is similar across building sites, though there is significant variance; (2) the expected accuracy of classifying the type of points required by APAR for a new unseen building is, on average, 75%; (3) the performance of the model does not decrease as long as training data and testing data are extracted from adjacent months. [ABSTRACT FROM AUTHOR]

The usage of Building Automation Systems (BAS) and Energy Management Systems (EMS) is indeed becoming ever more common and sophisticated, and seeking to promote energy savings by integrating new sources of data, such as user preferences, in real-time. This paper reviews the existing systems and proposes an innovation in HVAC systems management: a system that tracks the occupants’ preferences, learns from them, and manages HVAC automatically. Our hypothesis was that by developing a learning system based on feedback acquired through the mobile devices of room occupants to optimize the control of a HVAC system, in order to minimize energy consumption while maximizing average user comfort. A prototype solution is described and evaluated by simulation. We show that ambient intelligent systems can be used to control a building’s EMS, effectively reducing energy consumption while maintaining acceptable comfort levels. Our results indicate that employing a k-means machine learning technique enables the automatic configuration of an HVAC system to reduce energy consumption while keeping the majority of occupants within acceptable comfort levels. The developed prototype provides occupants with feedback on ambient variables on a mobile user interface. © 2017 Elsevier Science. All rights reserved. [ABSTRACT FROM AUTHOR]

In European Union (EU), buildings are the larger consumers of energy. To save energy, use of energy in buildings must be effectively controlled on the basis of accurate field information, acquired from indoor and outdoor sensors. In this work an all-round management approach is proposed to simultaneously control in a building all types of load, both thermal and electrical. To this aim, a load simulator implementing a novel analysis and simulation method of customer's electric energy usage is illustrated. The model provides an estimated daily load profile by following a bottom-up approach through procedures each focused on simulating a specific energy usage in the building. The paper reports some of the main flow charts of the simulation model, as well as possible applications of the program for the prediction of the response of buildings to different demand management strategies. The suggested on-line management system was also validated through field experimental tests and a case study, whose results are reported and discussed. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved. [ABSTRACT FROM AUTHOR]

Optimization in terms of functionality, comfort and people's safety, while also ensuring cost minimization may imply automation, including building automation, which can be achieved using Building Management System (BMS). This emerging area is yet to be explored and analyzed, with various approaches in practice, yet little scientific analyses available, especially in terms of system architecture. Based on most frequent functions encountered in office buildings from Romania, we propose a minimal web-based architecture for BMS using modular approach, which enables fast exchange of information among its various components and centralized surveillance of subsystems through minimal human operator intervention. The architecture proposed may be further developed so as to include additional functions in the respective type of building, moreover, such architecture may also be considered for use in buildings with other destinations, taking into account all the characteristics of each building. [ABSTRACT FROM AUTHOR]

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Practical building operations usually deviate from the designed building operational performance due to the wide existence of operating faults and improper control strategies. Great energy saving potential can be realized if inefficient or faulty operations are detected and amended in time. The vast amounts of building operational data collected by the Building Automation System have made it feasible to develop data-driven approaches to anomaly detection. Compared with supervised analytics, unsupervised anomaly detection is more practical in analyzing real-world building operational data, as anomaly labels are typically not available. Autoencoder is a very powerful method for the unsupervised learning of high-level data representations. Recent development in deep learning has endowed autoencoders with even greater capability in analyzing complex, high-dimensional and large-scale data. This study investigates the potential of autoencoders in detecting anomalies in building energy data. An autoencoder-based ensemble method is proposed while providing a comprehensive comparison on different autoencoder types and training schemes. Considering the unique learning mechanism of autoencoders, specific methods have been designed to evaluate the autoencoder performance. The research results can be used as foundation for building professionals to develop advanced tools for anomaly detection and performance benchmarking. [ABSTRACT FROM AUTHOR]

Building automation systems is becoming more vital, especially in regard to reduced building energy consumption. However, the accuracy of such systems in calculating building thermal loads is limited as they are unable to predict future thermal loads based on prevailing environmental factors. The current paper therefore seeks to improve the understanding of the interactions between outdoor meteorological data and building energy consumption through a statistical analysis. Using weather data collected by the Korean Meteorological Agency (KMA) over a period of three years (2011–2014), prediction models that are able to predict heating thermal loads considering the time-lag phenomenon are developed. In addition, the study develops different prediction models for buildings of different sizes. The results confirm the existence of the time-lag phenomenon: the heating load experienced by a building at a given time is better explained by a regression model developed using the climatic conditions that existed two hours before. As such, conventional building simulation programs must endeavor to include time-lag as well as Aerosol Optical Depth (AOD) data as important factors in the prediction of building heating loads. [ABSTRACT FROM AUTHOR]

The continued growth in variable renewable energy sources (VRES) has created increased focus on the use of office buildings for power flexibility activities. Office buildings uniquely present opportunities for relatively easy control adaptation during power flexibility activities given their large thermal inertial and existing building automation. Though a number of studies have outlined the potentials of office buildings for power flexibility, however only few studies have clearly outlined associated critical performance characteristics as it relates to comfort. Subsequently, this paper uses structured literature survey to outline critical performance characteristics that should be considered when using office buildings as power flexibility resources. Understanding the performance characteristics that are critical for using office buildings as power flexibility resources is important not only for their effective control and coordination but also to avoid compromising the core role of office buildings which is the provision of a comfortable and productive environment for business transactions. [ABSTRACT FROM AUTHOR]

Facility benchmarking and evaluation of facility performance are the crucial tasks in reaching efficient, economical and sustainable facility operation. Modern buildings are equipped with building automation systems (BAS) that contain vast numbers of various sensors that can be utilised in performance assessment. However, such systems lack convenient tools for data inspection, which limits their use in building performance and efficiency analysis and benchmarking especially on large sites. The paper presents a middleware layer designed to enrich BAS data with additional semantic information. As a semantic model, an adaptation of the Semantic Sensor Network (SSN) ontology for the field of building operation analysis is used. The middleware provides convenient interfaces for querying the model. The proposed system provides the facility managers with a convenient way to use the BAS data for benchmarking and decision support. [ABSTRACT FROM AUTHOR]

This paper presents a review of recent research and development on methodologies relevant to automating mapping of points in building control systems and between building control systems and external or replacement software and hardware. Manual point mapping is labor intensive and costly, presenting a major impediment to innovations in building control (e.g., automated fault detection and diagnostics, self-healing, and automated commissioning for existing building control systems). The methods reviewed focus on classifying building control system points, especially sensor classifications by sensor type. Fewer publications address other important aspects of the point mapping problem, such as discovering spatial and functional relationships among points, relationships between control system points, physical systems, and equipment, and between various equipment and the systems of which they are part, and discovering metadata, normalizing it to a common namespace, and assigning the metadata to control system points. To motivate further development of new automated point mapping approaches, we identify many research questions organized into four key technical needs: 1) a complete solution and underlying problem formulation, 2) alignment of methods with the actual point mapping problem, 3) test cases, data sets for testing, explicit test procedures, and consistent performance metrics for reporting testing and evaluation results, and 4) understanding of the applicable data space to ensure future adaptability of automated BAS point mapping. [ABSTRACT FROM AUTHOR]

Characteristics and problem of building automation system (BAS) existed in the practical engineering application are analyzed, and then the development of BAS is discussed from the perspective of network structure and supplied function service. Aiming at the space distribution feature, this paper presents the network architecture of Ubi-bus fieldbus, and then essential feature, operation mechanism and standardized information model are introduced respectively. Software and hardware are designed and developed in the control platform of variable air volume (VAV) air conditioning system. Based on the network architecture of Ubi-bus, it is potential for the solution of BAS integration in the practice application. [ABSTRACT FROM AUTHOR]

On/off controlled chiller is wildly used for small-scale air-conditioning (AC) system. For many existing systems, the chiller may be switched on and off frequently, which speeds up the wear and aging of the chiller and introduces significant disturbances on the space temperature control. This paper presents an experimental study on a control method which combines a set-point reset technique with a bilinear control technique. The set-point reset technique is adopted to reduce the on/off frequency of the chiller, and the bilinear control is used to track the space temperature set-point. The control method was realized in a building automation system (BAS) and applied to a test rig. The experimental results show that the set-point reset technique can reduce the chiller on/off frequency obviously. The bilinear control can achieve more robust space temperature control while comparing with conventional PI control. This proposed control method has the potential to be used in the existing on/off chiller integrated AC systems with the small chilled water volume. It is able to reduce the on/off frequency of the chiller efficiently and guarantee the robustness of the space temperature control simultaneously from the control role. [ABSTRACT FROM AUTHOR]

This work presents a new methodology for quantifying the discomfort caused by non-optimal temperature regulation, in a building automation system, as a result of degraded biomass boiler operation. This discomfort is incorporated in a model-based dynamic programming algorithm that computes the optimal maintenance action for cleaning or replacing the boiler. A non-linear cleaning model is used to represent the different cleaning strategies under taken by contractors. The maintenance strategy minimizes the total operational costs of the boiler, the cleaning costs and the newly defined discomfort costs, over a long-term prediction horizon that captures the short-term daily thermal comfort within the heating zone. The approach has been developed based on real data obtained from a biomass boiler at a Spanish school and the resulting optimal maintenance strategies are shown to have the potential of significant energy and cost savings. [ABSTRACT FROM AUTHOR]

Previous research efforts, for optimizing energy usage of HVAC systems, require either mathematical models of HVAC systems to be built or they require substantial historical operational data for learning optimal operational settings. We introduce a model-free control policy that begins learning optimal settings with no prior historical data and optimizes HVAC operations. The control policy is an adaptive hybrid metaheuristic that uses real-time data, stored in building automation systems (e.g., gas/electricity consumption, weather, and occupancy). It finds optimal setpoints at the building level and controls setpoints accordingly. The algorithm consists of metaheuristic (k-nearest neighbor stochastic hill climbing), machine learning (regression decision tree), and self-tuning (recursive brute-force search) components. The control policy uses smart selection of daily setpoints as its control basis, making the control schema complementary to legacy building management systems. To evaluate our approach, we used the DOE reference small office building in all U.S. climate zones and simulated different control policies using EnergyPlus. The proposed algorithm resulted in 31.17% energy savings compared to the baseline operations (22.5 °C and 3 K). The algorithm has a superior performance in all climate zones for the goodness of measure (i.e., normalized root mean square error) with a value of 0.047. [ABSTRACT FROM AUTHOR]

Used and developed initially for the IT industry, the Cloud computing and Internet of Things concepts are found at this moment in a lot of sectors of activity, building industry being one of them. These are defined like a global computing, monitoring and analyze network, which is composed of hardware and software resources, with the feature of allocating and dynamically relocating the shared resources, in accordance with user requirements. Data analysis and process optimization techniques based on these new concepts are used increasingly more in the buildings industry area, especially for an optimal operations of the buildings installations and also for increasing occupants comfort. The multitude of building data taken from HVAC sensor, from automation and control systems and from the other systems connected to the network are optimally managed by these new analysis techniques. Through analysis techniques can be identified and manage the issues the arise in operation of building installations like critical alarms, nonfunctional equipment, issues regarding the occupants comfort, for example the upper and lower temperature deviation to the set point and other issues related to equipment maintenance. In this study, a new approach regarding building control is presented and also a generalized methodology for applying data analysis to building services data is described. This methodology is then demonstrated using two case studies. [ABSTRACT FROM AUTHOR]