Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence

Tushar, Wayes; Wijerathne, Nipun; Li, Wen‐Tai; Yuen, Chau; Poor, H. Vincent; Saha, Tapan Kumar; Wood, Kristin L.

doi:10.1109/msp.2018.2842096

Cited by 107 publications

(44 citation statements)

References 28 publications

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“…In addition to the above mentioned, many other cases have been studied on HVAC [18][19][20][21][22][23][24][25][26][27]. In the present paper, a model predictive control (MPC) method has been used for controlling temperature and humidity which was previously only introduced for controlling temperature in [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.

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Section: Related Workmentioning

confidence: 99%

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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“…Alike green IT, the purpose of greening software as shown in Figure 9 is to reduce its negative impact on the environment. In addition, developers of Green APIs provides potential FIGURE 9 Green APIs monitoring and control system with the combination of Machine Learning (ML) 162 to perform compound analytics and Artificial Intelligence (AI) 163 for the prediction of available energy. Subsequently, different types of users can access the data and services from their specified applications such as smart phones, mobile devices, websites, connected appliances, cloud apps, web portals, gaming consoles, and tablets.…”

Section: Green Software (Apis)mentioning

confidence: 99%

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

Moin

Karim

Safdar

et al. 2018

Concurrency and Computation

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Network architectures nowadays are not able to address the requirements of future Internet, as they are not sufficient, ossified, and are power hungry on the TCP/IP paradigm. Therefore, to promote the realizable progression toward the new paradigms and modern protocols, modern routers are required to become programmable for the flexible control and dynamic management of both power states and traffic flows. In this scenario, Software Defined Networking (SDN) is considered as an exemplary approach for providing the support of dynamic traffic flows of each node on a network. Besides the benefits that SDN brought to the networking area, there are still crucial challenges such as to design an effective, lightweight API, rich algorithms, and traffic forwarding rules for SDN layers along with the adoption of an energy efficient infrastructure that could have the least effect on the environment. The aim of this research is to drive the attention toward environment friendly adoption and implementation of SDN that could deal with the growing number of users and network equipment. For this purpose, in this research, a comprehensive review on SDN while mapping the characteristics of green computing with SDN is provided. In addition to that, the research stressed on the benefits of Green SDN through a proposed G-SDN framework. Some well-defined case studies are presented to highlight the need of greening an SDN infrastructure. Finally, various research directions are presented to the wide adoption of SDN technology for academia and industry alike. KEYWORDS green computing, green SDN, SDN, software defined networks 1 INTRODUCTION Software Defined Networking (SDN) offers a high level of flexibility, controllability, and programmability in networks with the power of centralized control. The centralized controller at SDN faces a significant performance drawback when dealing with huge network flows. 1 On the other hand, features provided by SDN allows an ease in deploying the strategies that are designed to overcome the existing problems of power and energy-waste. 2 Green computing refers to the reduction of hazard and toxic materials, improved efficiency of energy including recycling of factory wastage, 3 and it has become an integral part to enhance the reliability of each emerging computing domains. Even the cloud and green computing are applicable side by side to make greener internet and enhance services. The former techniques allow the services with ease when required at a cost of complex infrastructure. However, the later approach has edge in providing all of these services with efficient energy usage, recyclability, low environmental impact, reduced carbon emission, and much more facilities. 4 In addition, to provide the promising services to the intended users, wired and wireless networks 5,6 have been followed by many directions in prior studies 5-7 such as the closing of forward and feedback loops. 8 In this way, there exists a variety of designs such as low-power solutions with slow and fast dynamics. Solutions with sl...

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“…Recently, the Internet of Things (IoT) as a prominent technology is transferring conventional building energy management systems (BEMS) into smart, scalable, efficient, secure, flexible, and real-time systems for easier and greater energy-savings in both residential [28][29][30][31][32] and commercial buildings [33][34][35][36][37][38]. In particular, IoT-based approaches more accurately estimate thermal and scheduling models to minimize energy used by HVAC systems [39][40][41][42][43][44]; these systems currently consume about 50% of building energy consumption in developed countries [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

2020

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Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants.Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings.iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants' EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

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Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence

Cited by 107 publications

References 28 publications

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

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