Article

WinLight: A WiFi-based occupancy-driven lighting control system for smart building

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Abstract

Artificial lighting accounts for a significant proportion (19%) of energy consumption in building environments. This large contribution calls for the creation of energy-efficient lighting control schemes. In this article, we present WinLight, a novel occupancy-driven lighting control system that aims to reduce energy consumption while simultaneously preserving the lighting comfort of occupants. By leveraging the fine-grained occupancy information estimated by existing WiFi infrastructure in a non-intrusive manner, WinLight computes an appropriate dimming command for each lamp based on a novel lighting control algorithm. A centralized lighting control system assigns these commands to a zonal gateway, and occupancy-driven lighting control is achieved by actuating the brightness adjustment with a local controller integrated within each lamp. Moreover, a WinLight App is designed to enable occupants to customize their luminance preferences and to control nearby lamps using their mobile devices. We implemented WinLight in a 1500 m2 multi-functional office in Singapore and conducted an experiment during 24 weeks. The experimental results demonstrate that WinLight achieves 93.09% and 80.27% energy savings compared to static scheduling lighting control scheme and PIR sensor based lighting control scheme while guaranteeing the personalized lighting comfort of each occupant.

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... On the other hand, intelligent lighting [14] has contributed to energy savings by about 19 % of total of building energy consumptions [5,15]. This has encouraged researchers to work on enhancing this percentage by proposing various systems for smart cities [16], residential buildings [17], or commercial buildings [18] either by using intelligent [19], adaptive [20], or artificial neural network based [16]. ...
... Occupancy-based LSs contribute much in energy savings for buildings and offices. In literature, many studies have proposed different ways to utilize occupants' profiles and preferences to efficiently control lighting energy systems [15,34,35]. One of the occupants' preferences related systems is explained in [34]. ...
... In [15,35], both research studies have aimed to propose an intelligent LSs. The system proposed in [35] is more dedicated for smart lighting control inside buildings to which energy savings and occupants' comfort conditions are considered. ...
... Jia (2016) [50] developed a platform MapSentinel which combines WiFi sensing networks, ultrasonic calibration stations, and floor map processing engine to improve the accuracy in indoor tracking. Weekly (2018) [51] developed Building-in-Briefcase (BiB), an environmental sensor suite that used passive infrared (PIR) motion sensor to detect occupancy, while Zou (2018) [52] argued that their WiFi-based non-intrusive Occupancy Sensing System (WinOSS) could achieve almost 100% accuracy in occupancy detection which is better than BiB. There is a clear trend from a time perspective: from the early study on decision support for smart buildings to the technological problems in operating smart buildings and recently incorporating occupancy information in smart building management. ...
... Hart (1992) [63] and Zoha (2012) [64] set the root for non-intrusive load monitoring of subsystems of a building, including the lighting system. Bourgeois (2006) [65] and [52] added an occupant component to advanced lighting control. The blue cluster is about occupant behavior modeling for building performance simulation. ...
... The active authors cover a variety of topics, but half of them focus on automatic lighting control using occupants' information [52,80,81]. The first two early-time publications provided fundamental contributions to this field and received relatively high citations, i.e., how to select proper intelligent systems [31] and how to enable interactions between occupants and the environment [49]. ...
Article
This paper reviews the state-of-the-art smart building research by a bibliometric analysis, a content analysis, and a qualitative review. The bibliometric analysis of 364 academic papers shows that smart building is a burgeoning, interdisciplinary field with a relatively high international collaboration level. Keyword's clustering identified two major themes: (1) IoT, WSN, and cloud computing for automation control and (2) the balance between energy efficiency and human comfort based on continuous monitoring and machine learning. The content analysis statistically detected a transition from the cyber-physical system (CPS) to the human-cyber-physical system (HCPS) in smart building research. We therefore proposed an HCPS framework with three dimensions—cyber-physical scale, human needs, and human roles—to summarize current research and discover potential gaps. Under this framework, five HCPS future research directions for occupants-centered smart buildings were proposed: adaptive building envelope, integrated building management system, enhanced building energy management, adaptive thermal comfort, and microgrid adoption.
... As mentioned earlier, a potential benefit of smart lighting systems is energy savings. Previous studies have focused on energy saving through light-emitting diodes (LED) [6,7]. These smart lighting systems, as compared to traditional lighting systems, are installed in office locations and have high potential for energy savings (i.e., 17-60% depending on usage patterns) [8]. ...
... Zou et al. [7] proposed a smart lighting control system that adjusts the brightness level of lamps based on real time occupancy data to minimize energy consumption. They included personalization by an app control feature that enables the occupant to adjust the brightness level of a nearby lamp. ...
... To do so, we first compare different algorithms and their prediction performance for each of our classes (colors). We used precision, recall, F-score, accuracy and balanced accuracy in our framework for evaluation purposes in Equations (3)(4)(5)(6)(7)(8) • ...
Preprint
Recommender systems attempts to identify and recommend the most preferable item (product-service) to an individual user. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system that leverages a wealth of historical data and machine learning methods. We introduce an unsupervised method to recommend a routine for lighting. Moreover, by analyzing users' daily logs, geographical location, temporal and usage information we understand user preference and predict their preferred color for lights. To do so, we cluster users based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.
... B UILDINGS play an important role in our lives. People usually spend in average 20 hours per day inside buildings [1]. Also, the number of inhabitants in urban areas is quickly increasing [2]. ...
... Some of them collect information on the building areas occupancy history to predict if they are occupied or not (occupancy detection) [3], [5], [8], [10]. Others use Wi-Fi information to predict the occupancy count of some building areas [1], [4], [6], [9]. In this scenario, several studies use Wi-Fi infrastructure combined with machine learning methods to predict occupancy of building areas, floors and rooms [1], [3]- [5], [8], [10], [11]. ...
... Others use Wi-Fi information to predict the occupancy count of some building areas [1], [4], [6], [9]. In this scenario, several studies use Wi-Fi infrastructure combined with machine learning methods to predict occupancy of building areas, floors and rooms [1], [3]- [5], [8], [10], [11]. They do not necessarily use the association history information from the Wi-Fi network to build their dataset and create prediction systems, but rather other information such as channel utilization or bandwidth [1], [3]- [5], [8], [10]- [13]. ...
Article
Full-text available
People usually spend several hours per day inside buildings, and they require great amounts of energy and resources to operate. Although there are numerous studies about smart buildings, there is still a need for new intelligent techniques for efficient smart building management. This paper proposes the use of Wi-Fi network association information as a basis for the design of intelligent systems for smart buildings.We propose a unified experimental methodology to evaluate machine learning (ML) models on their capacity to accurately predict Wi-Fi access point demand for energy-efficient smart buildings. The evaluation involves the use of multiple classification and regression models using a variety of configurations and algorithms. We conducted an experimental analysis using our proposed methodology to determine which ML models provide the best performance results using data collected from a large scale Wi-Fi network located at Fluminense Federal University (UFF) over a period of 6 months. The proposed methodology enables the user to evaluate and to create ML models for energy efficient smart building management systems. We achieved 86.69% accuracy for occupancy prediction using classification techniques and RMSPE (Root Mean Squared Percentage Error) of 0.29 for occupancy count prediction using regression techniques.
... Współczesny człowiek spędza większość czasu w budynkach, które stanowią jego bezpośrednie otoczenie. Szacuje się, średnio jest to 20 godzin na dzień [28], a w krajach rozwiniętych nawet niemal 22 godziny dziennie [29]. W związku z tym jedną z podstawowych funkcji, którą powinny realizować budynki jest zapewnianie komfortowych warunków pracy wzrokowej ludzi, niezależnie od ilości dostępnego światła naturalnego. ...
... W USA udział oświetlenia w narodowym zużyciu energii wynosi 7% [147], w Szwecji 10% [87], a we Włoszech 16,4% [147,153]. Biorąc pod uwagę samą energię elektryczną produkowaną na świecie, oświetlenie ma od 19% [144,147,154,28,32,53], 20% [57,86], do 20-30% [155] udziału w jej zużyciu. W krajach uprzemysłowionych do oświetlania wykorzystuje się 5-15% wytwarzanej energii elektrycznej [156]. ...
... Ze względu na sposób organizacji, systemy automatycznego sterowania oświetleniem można podzielić na [28]: ...
Thesis
Full-text available
W pracy zawarto opisy analiz, badań symulacyjnych, ankietowych i doświadczalnych w zakresie możliwości poprawy efektywności energetycznej oświetlenia, przy użyciu nowoczesnej instalacji elektrycznej stosowanej w budownictwie biurowym typu smart. Scharakteryzowano różne metody sterowania oświetleniem, przeanalizowano ich energooszczędność, a także zwrócono uwagę na aspekt akceptacji przez ludzi systemów automatycznego sterowania. Wykazano, że indywidualne preferencje oświetleniowe ludzi są zróżnicowane, a w biurach znacząca liczba osób preferuje natężenie oświetlenia niższe niż rekomendowane. Zaproponowano metodę lokalnego, spersonalizowanego sterowania oświetleniem w przestrzeni biurowej typu open space, która wykorzystując matrycę niezależnie sterowanych lamp LED oświetlenia ogólnego, łączy regulację indywidualną według osobistych preferencji oraz sterowanie zależne od zajętości poszczególnych stanowisk. Optymalizację tego typu problemu sterowania przeprowadzono na przykładzie wybranego pomieszczenia doświadczalnego w oparciu o dwa modele matematyczne systemu oświetleniowego: liniowy i wykorzystujący sztuczne sieci neuronowe. Rezultaty wykazały, że proponowana metoda sterowania umożliwia poprawę efektywności energetycznej pracy systemu oświetleniowego oraz warunków oświetleniowych wytwarzanych na stanowiskach pracy, względem tradycyjnej instalacji oświetleniowej.
... Simulation can be used to evaluate how resourceful is the smart building by considering its distinctive characteristic [12]. Artificial lighting accounts for 19% of energy consumption in the building and that is why Zou et al. [13] introduced a Wi-Fi-based occupancy-driven lighting control system that is aiming the management of energy usage in operation phase [13]. Another proposal presented an optimal strategy to control energy flow for a Residential Energy Local Network (RELN) based on the idea of demand-side management [14]. ...
... Simulation can be used to evaluate how resourceful is the smart building by considering its distinctive characteristic [12]. Artificial lighting accounts for 19% of energy consumption in the building and that is why Zou et al. [13] introduced a Wi-Fi-based occupancy-driven lighting control system that is aiming the management of energy usage in operation phase [13]. Another proposal presented an optimal strategy to control energy flow for a Residential Energy Local Network (RELN) based on the idea of demand-side management [14]. ...
... A case study of 31 options was conducted by applying this framework showing that energy simulation results of the base model are close to national reports mentioned in section 3.1 [26] with 36% electricity usage by the cooling system and 31% by the lighting sector (Table 4). Although artificial lighting addressed as 19% of energy consumption in the building by Zou et al. [13], this percentage represents lighting percentage in a building where electricity is the only energy source. In this research, electricity and gas were energy sources of the building. ...
Article
Development of Building Information Modelling (BIM) is assisting engineers with automating design/construction processes in the Architectural, Engineering and Construction (AEC) industry. Lack of such a comprehensive decision-making framework which utilizes BIM, Management Information Systems (MIS), simulation, and automation tools to choose between different construction alternatives have been addressed in the research background. As an example of decision-making objective, optimized smart building's equipment combination for a certain project should be selected in the feasibility study phase. In this research, a comprehensive decision-making framework was developed to choose smart building's equipment based on energy consumption and cost trade-off. Subsequently, smart building alternatives were considered as a decision-making example to choose the best alternative using BIM, MIS and simulation tools. The research contributes to the automating of some parts of the decision-making framework by developing an Application Programming Interface (API). This API helps with making the appropriate automating permutation out of possible options, totalizing the cost of each combination and sorting data in the research database developed for the recommended framework. This framework, database and API can be used similarly for any other decision making objective. Finally, a model representing the average conditions of residential buildings in Tehran was developed to choose the optimized smart building's equipment combination of 31 considered options. This optimized combination which included all possible smart building options except for smart lighting has an investment return of about 7.5 years which is more than that of similar projects in other countries due to Iran's low energy carrier tariff.
... Occupancy sensing has been widely studied, and many sensing techniques have been proposed, including PIR sensors [18,19], microwave Doppler sensors [20], ultrasound sensors [21], and Wi-Fi sensors [22]. PIR sensors have been widely used in the field of occupancy detection because they are fast and cost effective [23]. ...
... The Wi-Fi access points detect the occupancy presence using the Wi-Fi signals transmitted by mobile devices. The disadvantage of this approach is that occupants are required to carry their mobile devices all the time [22]. Other sensors, including CO 2 sensors [27] and camera [28], can also be utilized for occupancy detection. ...
... In smart lighting control, occupant detection is crucial to control strategy and energy savings. Several sensor technologies have been utilized to detect occupants, including PIR [34], mobile devices [22], and the combinations of PIR and microwave, PIR, and ultrasonic [35]. In most studies, sensors colocated with luminaires and detected occupant presence at individual workspace level instead of zone level and room level [36,37]. ...
Article
Full-text available
Buildings have been an important energy consuming sector, and inefficient controlling of lights can result in wastage of energy in buildings. The aim of the study is to reduce energy consumption by implementing a smart lighting system that integrates sensor technologies, a distributed wireless sensor network (WSN) using ZigBee protocol, and illumination control rules. A sensing module consists of occupancy sensors, including passive infrared (PIR) sensors and microwave Doppler sensors, an ambient light sensor, and lighting control rules. The dimming level of each luminaire is controlled by rules taking into consideration occupancy and daylight harvesting. The performance of the proposed system is evaluated in two scenarios, a metro station and an office room, and the average energy savings are about 45% and 36%, respectively. The effects of different factors on energy savings are analyzed, including people flow density, weather, desired illuminance, and the number of people in a room. Experimental results demonstrate the robustness of the proposed system and its ability to save energy consumption. The study can benefit the development of intelligent and sustainable buildings.
... Smart lighting management strategies proposed the achieved energy savings of about 44% compared to conventional systems [19]. The WinLight app. was designed to enable occupants to customize their luminance preferences and use their mobile devices to control nearby lighting [20]. ...
... compared to the fixed scheduled lighting control scheme and PIR sensor-based lighting control scheme, thus maintaining the comfort of the lighting allocated to the occupant. [20]. ...
Article
Full-text available
Egypt is striving to improve energy exports and efficiency. Smart Buildings (SBs) promote sustainability and represents an essential pillar of smart cities. Smart Systems (SSs) are the physical infrastructure of SBs, serving the integration between their components and the users. Smart energy systems (SESs) are among the essential SSs in SBs. This article's aim is boosting sustainability, relying on increasing energy efficiency by harnessing the SESs, which divided into three key divisions: Smart energy management (SEMS), smart energy generating (SEGSs), and smart energy consumption (SECSs). It concluded the features of SESs to analyze several international and local case studies. Then, it developed and checked 35 initiatives of SESs, relying on in-depth local surveys, to address smartness features in energy systems (ESs) according to their priority, availability, and cost. These initiatives represent an appropriate and customized guide to architects, stakeholders, and decision-makers, especially in the Egyptian scope. The study results concluded that only 22 initiatives were achieved by 62.86% and addressed their efficiencies. The discussion interpreted losing the 16 absent and weak initiatives regarding the technical, economic, and social obstacles to strengthen the current Egyptian unprecedented urban development decision-making
... Protocols like Bluetooth Low Energy (BLE) have the advantage of consuming an Recent literature reviews on smart lighting and controls showed that the application of smart lighting systems is mainly conducted in non-residential environments, focusing on energy savings [6][7][8]. Studies in office environments exhibited potentials for energy saving varying from 17 to 94% over traditional (manual) control systems, depending on user behaviour, activity patterns, and different types of control systems, such as daylight harvesting and occupancy control systems [6,[9][10][11][12][13][14][15]. Control systems based on occupancysensing are commonly used for energy saving by detecting the user's motion in the targeted environment. ...
... To benefit from lighting products' smart Recent literature reviews on smart lighting and controls showed that the application of smart lighting systems is mainly conducted in non-residential environments, focusing on energy savings [6][7][8]. Studies in office environments exhibited potentials for energy saving varying from 17 to 94% over traditional (manual) control systems, depending on user behaviour, activity patterns, and different types of control systems, such as daylight harvesting and occupancy control systems [6,[9][10][11][12][13][14][15]. Control systems based on occupancysensing are commonly used for energy saving by detecting the user's motion in the targeted environment. ...
Article
Full-text available
The idea of smart lighting has emerged over the years in commercial and industrial environments, with a focus on energy saving. With the advancement in technology, smart lighting can now offer opportunities in addition to energy saving to users in home environments for the provision of a comfortable atmosphere and the maintenance of user well-being. Currently, research in the smart lighting field is predominantly dedicated to energy saving in non-residential environments; meanwhile, the residential environments have not been explored. Therefore, a literature review was conducted to provide an overview of smart lighting systems’ effect on energy and well-being in the residential environment. Current research is mostly limited to designing and developing a smart lighting system in a controlled environment, with a limited evaluation of well-being and comfort. The review shows that residential smart lighting application possibilities and opportunities are not widely and thoroughly explored.
... Some of the sensor technologies used are PIR, WiFi-based and light sensor. Studies [36], [37] and [38] are experiment based while studies [39] and [40]are simulations. Despite being motivated by different objectives, the studies in common adopt the following methods of occupancy-based and illuminance-based control strategies: ...
... i. WiFi based sensor Zou et al. [36] proposed a centralised occupancy based lighting control system by using WiFi system for occupancy detection. This allow for zones control by adjusting the brightness of lights automatically in real time based on the occupancy information gathered. ...
Article
Human factor has a significant influence in space and energy utilisation in buildings. Space utilisation refers to a measure of whether space of a building is being used and how it is being used. While energy utilisation refers to the amount of energy being used. As space and energy of buildings are mostly dominated by the occupancy and related activities, occupancy information is vital in managing space and energy use. By detecting the presence of an occupant in a building or occupancy detection, space and energy can be utilised efficiently by corresponding to the occupant’s dimension and activities, thus avoiding wastage. Accordingly, through various methods, occupancy detection employs various sensors available in term of spatial temporal properties depending on the area to be deployed. Majority of past studies were focused on the aspect of energy utilisation and occupancy and there is lack of review paper which gathers information on occupancy detection inrelation to both space and energy utilisation.Therefore, this paper aims to develop a taxonomy of occupancy detection technologies for efficient space and energy utilisation of buildings. A survey of past studies on space and energy utilisation of buildings in relation to occupancy level are done to understand the relationship between occupancy and space and energy utilisation, respectively. A taxonomy is then built to identify the technologies which are currently available for occupancy detection. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
... Variation in ambient conditions (e.g., air temperature, humidity, CO2, acoustic, and light) can be used to infer occupancy information [28,47,48,[50][51][52][56][57][58][59]. One advantage of methods based on the variation in ambient conditions over the motion sensors is that the number of occupants can be inferred. ...
... Finally, occupants' interaction with furniture (e.g., chairs, doors) [39,57,66], appliances (e.g., computers) [40,67], or communication networks (Wi-Fi, Bluetooth) [58,68,69] or corresponding plug loads [70,71] can be used to estimate occupancy information. The collection of the above data requires no or minimal sensors or devices; estimation based on them could be promising for real-world applications. ...
... In addition, it is almost impossible to obtain information for a large number of buildings because of the high cost of occupant counting sensors. Nonetheless, with the development of the Internet of Things, researchers are considering data from mobile phones and Wi-Fi-based sensor data [25,26]. Recent research on urban mobility has uncovered the potential of deriving occupancy locations using mobile positioning data [12,27]. ...
Article
In the US, people spend more than 90% of their time in buildings, which contributes to more than 70% of overall electricity usage in the country. Occupant behavior is becoming a leading factor impacting energy consumption in buildings. Existing occupant-behavior studies are often limited to a single building and individual behavior, such as presence or interactions in confined spaces. Moreover, studies modeling occupant behavior at the building or community level are limited. With the development of the Internet of Things, mobile positioning data are available through social media and location-based service applications. The goal of this study is to analyze the impacts of more representative occupancy profiles, derived from high resolution urban scale mobile position data, on building energy consumption. . A pilot study was conducted on more than 900 buildings in downtown San Antonio, Texas, with billions of mobile positioning data. We then compared these profiles with the existing Department of Energy prototype models and quantified the differences using a statistical method. On average, the differences in occupancy rates between the ones derived from the empirical profile and the ones from the Department of Energy reference ranged from −30% to 70%. The realistic derived profiles are then simulated in the CityBES. The results show that the predicted cooling energy demand is reduced by up to 40% while the heating energy demand is reduced by up to 60%. This study, therefore, advances knowledge of urban planning as well as urban-scale energy modeling and optimization.
... Low-cost, non-invasive technologies for occupancy detection and people counting could bring about new applications across a range of scenarios. For example, they could be used to optimise energy use in intelligent buildings by reducing unnecessary lighting and heating [1], or monitoring queues and seated people in shopping malls and restaurants to inform staff resourcing [2]. In public transportation, estimating the number of passengers on a train platform or bus stop could be used to adjust scheduling arrangements to minimise overcrowding while in security and law enforcement, occupancy detection can be employed to ensure no one has entered an area after it has been cleared by security services [3]. ...
Conference Paper
Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements.
... These studies seek to minimize the lighting consumption while maintaining the desired illuminance level by users from to luminous flow regulation of luminaries [8,15,[24][25][26][27][28]30,[32][33][34][35]. Therefore, the solutions are characterized by high controllability of every luminary, which reduces its demanded power for every time. ...
Article
The use of daylighting reduces energy consumption in buildings, which can be achieved by the implementation of the hybrid illumination systems (HIS). In general, the solutions of HIS exercise high controllability on every luminary for minimizing the energy consumption while maintaining the desired illuminance level by users. Nevertheless, it is only considered the demanded power by luminaries despite to auxiliary components (e.g. sensors, power-packs, and controllers) operate full time and their energy consumption may be representative. Therefore, this study proposes a procedure for sizing HIS for inner spaces with the lowest annual energy consumption (AECHIS) based on an exhaustive search. All of the possible control alternatives are determined by a combinatory process considering calculation using Stirling number of the second kind, the number of control groups, and the number of available control strategies. The modelling of interior illuminance is based on the daylight factor matrices, the incident solar irradiation on each opening, the illuminance contribution by luminaries, and the operation conditions of control alternative. AECHIS is defined as a function of number and power consumed by luminaries (Plum) and auxiliary components (Paux) considering operating conditions of HIS for every time-interval. Plum is defined as an MINLP problem that depends on the state (On or Off) and luminous flow percentage of every luminary. The procedure is applied to two inner spaces of the university building: a meeting room and a classroom, from the integration of MATLAB and GAMS. Results show the AECHIS for all possible alternatives and the description of the technical characteristics for the best control alternative. Also, it can be evidenced that the energy consumption by auxiliary components influences notably on sizing process because represents for majority of cases between 10% and 50% of AECHIS, the reason why the option with the highest controllability is not the best solution.
... The energy consumed for the lighting of buildings constitutes a large part of the energy consumption. Electricity lighting covers 19 % of the total electricity production in the world [3]. Commercial and office buildings are in the group of high energy-consuming buildings. ...
Article
In this study, design and implementation of a new cost-efficient daylight-based lighting control system is proposed to provide energy saving in a public building with a conventional lighting system. Energy gain recovery and regional daylight utilization coefficients are obtained by conducting daylight measurements in all indoor spaces of the building where the proposed lighting system will be applied. Daylight value is continuously transferred to the control system through the pyranometer placed outside and the need for artificial lighting is calculated by using sectional daylight utilization coefficients. Thereby, maximum benefit from daylight is realized when unnecessary energy consumption for artificial lighting is reduced. Experimental measurement results show that the proposed daylight-based lighting control system provides an average energy efficiency of the building at the level of 60 %. Additionally, the required investment, such as operating cost and payback period for converting an existing conventional lighting system into the proposed system, are discussed in detail. Cost analysis shows that the payback period of the proposed system can be reduced by 5 years compared to the conventional system.
... The participants also gave comments on desired features, like changing light profile preferences (e.g., warm/cold). Generally, the possibility to personalize lighting is positively perceived in related work [74]. The participants also indicated the desire to use a similar control system in other spaces, like home. ...
Article
Full-text available
Advances in technology and data analysis provide rich opportunities for developing intelligent environments assisting their inhabitants, so-called smart environments or smart spaces. Enhanced with technology, sensors, user interfaces, and various applications, such smart spaces are capable of recognizing users and situations they are in, react accordingly, e.g., by providing certain services or changes to the environment itself. Therefore, smart space solutions are gradually coming to different application domains, each with corresponding specific characteristics. In this article, we discuss our experiences and explore the challenges of a long-term real-world Internet of Things (IoT) deployment at a University campus. We demonstrate the technical implementation and data quality issues. We conduct several studies, from data analysis to interaction with space, utilizing the developed infrastructure, and we also share our actions to open the data for education purposes and discuss their outcomes. With this article, we aim to share our experience and provide real-world lessons learned when building an open, multipurpose, publicly used smart space at a University campus.
... Gu et al. (2018) uses clustering methods to extract typical daily occupant behaviour patterns in public buildings. Zou et al. (2018) analysed WIFI-based data for occupant behaviour prediction and lighting control. These researches provide insights for mega data analysis in building occupant behaviour research. ...
... With the development of wireless communication technology, Wi-Fi has become another widely used load tracking method. Zou et al. [11] developed a Wi-Fi-based smart building load tracking system that supported real-time consumption data verification and control. This is an efficient indoor load tracking system. ...
Article
A system that combines the advantage of the long-range (LoRa) communication method and the structural characteristics of a mesh network for an LoRa mesh network-based wireless electrical load tracking system is proposed. The system demonstrates considerable potential in reducing data loss due to environmental factors in far-field wireless energy monitoring. The proposed system can automatically control the function of each node by confirming the data source and eventually adjust the system structure according to real-time monitoring data without manual intervention. To further improve the sustainability of the system in outdoor environments, a standby equipment is designed to automatically ensure the normal operation of the system when the hardware of the base station fails. Our system is based on the Arduino board, which lowers the production cost and provides a simple manufacturing process. After conducting a long-term monitoring of a near-field smart manufacturing process in South Korea and the far-field energy consumption of rural households in Tanzania, we have proven that the system can be implemented in most regions, neither confined to a specific geographic location nor limited by the development of local infrastructure. This system comprises a smart framework that improves the quality of energy monitoring. Finally, the proposed big-data-technology-based power supply policy offers a new approach for prolonging the power supply time of off-grid power plants, thereby providing a guideline for more rural areas with limited power sources to utilize uninterrupted electricity.
... Cetin monitored heating, ventilation, and air conditioning (HVAC) system operations in 189 homes through home energy-management systems, including ON-OFF operation patterns and energy consumption (Cetin and Novoselac, 2015). Zou used the Wi-Fi infrastructure to collect occupancy data non-intrusively in a multifunctional office (Zou et al., 2018). Chetty conducted human activity recognition based on smartphone inertial sensors (Chetty, White, and Akther, 2015). ...
Article
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Space cooling energy consumption is a significant component of building energy consumption, and in recent years it has attracted much attention worldwide owing to its significantly increasing usage. The variable refrigerant flow (VRF) system is one common type of cooling equipment for buildings in China and is applied extensively to residential and office buildings. The performance of VRF systems significantly influences the cooling energy consumption of buildings. The system energy efficiency and electricity consumption are the main indicators employed to evaluate the performance of VRF systems. It is hard to obtain the actual energy efficiency and electricity consumption of VRF systems in buildings because of the high cost of the required complicated measurements. This study proposes a virtual sensor modeling method to determine the actual energy efficiency and electricity consumption of 344 VRF systems in residential buildings. Statistical and clustering analyses are conducted to determine the energy efficiency and electricity consumption to obtain distributions and typical operation load patterns of VRF systems in residential buildings in China. The main findings are as follows: the main range of the Seasonal Energy Efficiency Ratio (SEER) for the cooling season is from 2.9 to 4.4; the median SEER in the Hot Summer and Cold Winter zone is lower than in another climate zones; the longer cooling duration may lead to greater electricity consumption, and the electricity load for VRF systems electricity load is periodic for each day. The oversizing issue is common for VRF systems in the dataset, which also led to the lower energy efficiency of VRF systems. The high usage of VRF systems appeared from July 27th to August 26th. The findings provide recommendations for designing VRF systems in residential buildings. keywords Variable Refrigerant Flow (VRF), Energy efficiency, Virtual Sensor, Residential Building
... De Silva et al. [5] proposed to integrate a lighting system with occupancy detectors and daylight sensors for fully automated operations of adjusting the lighting system. Zou et al. [6] presented a wireless occupancy-driven lighting control system to reduce energy consumption while simultaneously preserving the lighting comfort of occupants. Huang et al. [7] integrated CO2 and light sensors with a wireless sensor platform and developed an occupancy detection method that can achieve higher accuracy while keeping low cost and non-intrusiveness. ...
... Therefore, implicit understanding of user preferences while interacting with the system is a more practical and feasible solution in residential settings. Zou et al. (2018) proposed a smart lighting control system that adjusts the brightness level of lamps based on real time occupancy data to minimize energy consumption. They included personalization by an app control feature that enables the occupant to adjust the brightness level of a nearby lamp. ...
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... As shown in the Table 2 , the sensor impact related literature can be summarized based on five key aspects: sensors and controls, sensor types, sensor locations, sensor data, and sensor impact framework. [52] , [53] , [54] , [55] , [73] , [65] , [76] , [66] , [78] , [107] , [ Energy consumption [59] , [61] , [69] , [121] , [122] , [62] , [123] , [68] , [ • A unified framework must be developed to investigate the sensor effects on building control loops. This framework can help facilitate sensor deployment and configurations for practical implementation. ...
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... One of the first and most interesting cases is the New York Times Headquarters, where the dimming system linked to the occupancy detectors and the daylight availability results in a reduction of energy consumption of about 40% in the surface area near the façade [14]. Other noticeable examples can be observed for atriums [15,16] and office buildings [17,18], as well as other typologies [19,20], in which schedules [21,22], illuminance thresholds [23,24] and the effect of different dynamic control logics [12,25] were under analysis, showing converging results. ...
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... For instance, Dan et al. proposed a method for detecting walking direction by CSI [5]. H. Zou et al. proposed a WiFi-based occupancy sensing method for lightning control system [6]. Winterhalter, Wera, et al. [7] proposed an efficient approach by using RGB-D camera equipped smartphone or tablet for detecting the 2-dimensional floor map for further localization. ...
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... Asides deployment costs, it is also computationally expensive to use cameras as they require intensive image processing. Electromagnetic signals in the form of Bluetooth or Wi-Fi signals have given birth to various frameworks for occupancy information detection [22][23][24][25][26][27]. This sensing system is advantageous because it uses non-intrusive methods that leverage existing wireless network infrastructure such as Wi-Fi routers and mobile devices to passively infer occupant information. ...
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... Prior research has shown that by having access to accurate occupancy counts and patterns, building automation systems (BAS) can dynamically adjust and control the ventilation rates of the HVAC systems in different zones, resulting in up to 80% reduction in HVAC-related energy consumption (Brooks et al. 2015). Moreover, occupancy information is important for emergency evacuation (Filippoupolitis et al. 2016), security management (Chen et al. 2018), and controlling lighting systems (second highest source of energy consumption) of buildings (Zou et al. 2018a). ...
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This paper presents development of 24GHz millimeter wave smart radar for intelligent street lighting system. The developed radar operates at a frequency of 24GHz with 200MHz bandwidth and CW (continuous wave) mode. The two radars are used to cover the street in both directions and detect obstacles moving at the speed of more than 1 km/h including moving pedestrians. The radar detection controls the street lighting. Therefore, the energy-saving performance has been improved because the proposed smart lighting system works only when obstacles around the streetlight exist. © 2019 IEEE.
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In this paper, we aim to clarify the effectiveness of a thermal‐based sensor, which applies image recognition technique to thermal image to measure occupancy state. Our experiment, which was conducted at a single private office over 8 days, evaluated both energy‐saving rate of occupancy control and F‐measure, which is the comprehensive metric of measurement accuracy. The thermal‐based sensor increased the F‐measure and energy‐saving rate by 2.23% and 27.91%, respectively, compared with the passive infrared sensor, which measures the occupancy state on the basis of change in infrared temperature. Compared with an image‐based sensor, which applies image recognition technique to visible image, it can provide high privacy and security preserving capability and detect occupants under completely dark environment. However, the F‐measure and energy‐saving rate were decreased by 3.13% and 19.07%, respectively. Further results and discussion are included herein toward analysis and improvement of the thermal‐based sensor. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
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To launch energy-efficient and occupant-centric services in smart buildings, understanding occupant behavior is becoming an essential underpinning. Prevailing approaches mainly adopt ambient sensors, wearables or vision cameras, which suffer from insensitive perception, arduous deployment or privacy-leakage risks. Recently developed WiFi-based methods circumvent the above limitations, however, most of them require sufficient training samples and computational resources for a comprehensive understanding of behavior patterns. This scenario requires occupant cooperation and considerable energy consumption. In this paper, we propose the use of LT-WiOB, which is a lightweight triplet framework for WiFi-based occupant behavior recognition, that provides a cost-efficient and user-friendly solution. Technically, the novelties of LT-WiOB are threefold. For the insufficient data problem, an efficient triplet framework is presented to measure the embedding dependencies; it attempts to avoid the ambiguity of new samples from each class and allows only a few samples to be entered. Regarding inefficient computation, a lightweight convolutional module is specifically designed and implemented to reduce the model complexity. For unstable training, the triplet architecture is further enhanced by employing a novel triplet sampling strategy that creates useful triplet candidates. Extensive experiments were conducted for performance comparison under diverse environmental settings. The results showed that LT-WiOB realizes the best overall accuracy of 96.1% and achieves over 90% and 85% accuracy in the five-shot and one-shot conditions, respectively, with almost half of the computation cost being saved. These results verify the efficiency and feasibility of the proposed LT-WiOB model.
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A reliable occupancy prediction model plays a critical role in improving the performance of energy simulation and occupant-centric building operations. In general, occupancy and occupant activities differ by season, and it is important to account for the dynamic nature of occupancy in simulations and to propose energy-efficient strategies. The present work aims to develop a data mining-based framework, including feature selection and the establishment of seasonal-customized occupancy prediction (SCOP) models to predict the occupancy in buildings considering different seasons. In the proposed framework, the recursive feature elimination with cross-validation (RFECV) feature selection was first implemented to select the optimal variables concerning the highest prediction accuracy. Later, six machine learning (ML) algorithms were considered to establish four SCOP models to predict occupancy presence, and their prediction performances were compared in terms of prediction accuracy and computational cost. To evaluate the effectiveness of the developed data mining framework, it was applied to an apartment in Lyon, France. The results show that the RFECV process reduced the computational time while improving the ML models’ prediction performances. Additionally, the SCOP models could achieve higher prediction accuracy than the conventional prediction model measured by performance evaluation metrics of F-1 score and area under the curve. Among the considered ML models, the gradient-boosting decision tree, random forest, and artificial neural network showed better performances, achieving more than 85% accuracy in Summer, Fall, and Winter, and over 80% in Spring. The essence of the framework is valuable for developing strategies for building energy consumption estimation and higher-resolution occupancy level prediction, which are easily influenced by seasons.
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In this study, we clarify the effectiveness of the energy‐effective techniques for the occupancy lighting control that is based on an occupancy sensor. The time delay from detecting no occupant to turning off the light is used for performing the occupancy control to avoid erroneously turning off the light (hereafter referred to as false‐off). Our experiment was conducted at an actual home environment over 8 days. We evaluated the performance of an image‐based sensor, which detects the occupants using image‐processing techniques and time‐delay adaptation, which decreases the time delay without false‐off. Compared with a passive infrared sensor, the image‐based sensor increased the average sensing accuracy for occupants with small movements by 21.58% or more. In addition, compared with the no‐adaptation case, the time‐delay adaptation increased the rate of average lighting energy saving by 3.94% or more. However, the experimental results suggested that the parameter of the time‐delay adaptation should be carefully set in terms of the energy‐saving effect and false‐off. The analysis and its practical discussions are included in this paper to achieve further energy efficiency. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Preprint
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HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an energy conservation measure where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building's real occupancy is required to maximize the success of this measure. In addition, there is the need for a scalable way to estimate energy savings potential from energy conservation measures that is not limited by building specific parameters and experimental or simulation modeling investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of occupant-derived HVAC schedules and energy savings that leverages the ubiquity of energy smart meters and WiFi infrastructure in commercial buildings. We estimate the schedules by clustering WiFi-derived occupancy profiles and, energy savings by shifting ramp-up and setback times observed in typical/measured load profiles obtained by clustering smart meter energy profiles. Our case-study results with five buildings over seven months show an average of 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy. We validate our method with results from building energy performance simulation (BEPS) and find that estimated average savings of MARTINI are within 0.9%-2.4% of the BEPS predictions. In the absence of occupancy information, we can still estimate potential savings from increasing ramp-up time and decreasing setback start time. In 51 academic buildings, we find savings potentials between 1%-5%.
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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.
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Occupancy schedule is one of the essential inputs for building performance simulation. Current designers and researchers refer to the occupancy schedules from energy standards such as ASHRAE 90.1, which was initially published in 1989 and may not apply to the occupancy profiles of the current circumstances. With the advances of mobile communication networks and positioning services, the mobile positioning data has been made available for researchers to obtain and extract real occupancy profiles for buildings of various types. This research utilizes mobile positioning data from social media platforms to extract typical weekly occupancy profiles of non-residential buildings by cluster analysis. The paper investigated the temporal distributions and heterogeneous features for typical profiles with the perspective of two descriptive parameters: peak ratio and daily total occupancy ratio, which represent the deviation of occupancy across the different days of a week. The proposed typical profiles are then compared with the reference profiles from ASRHAE Standards and the impact on energy simulation results is evaluated. Results suggests significant difference on energy load profiles and load distributions with the real occupancy profiles, and this method can prominently contribute to optimal building design strategies.
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