IoT-Based Smart Building Environment Service for Occupants’ Thermal Comfort

Park, Herie; Rhee, Sang-Bong

doi:10.1155/2018/1757409

Cited by 48 publications

(33 citation statements)

References 16 publications

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“…Park and Rhee [61] created a complete smart building system, taking care of the three layers of the IoT system. The equipment includes electrical devices and application, smart HVAC systems and personal wristband and smartphone application.…”

Section: Demonstration Studiesmentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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This paper presents a review of technologies under the paradigm 4.0 applied to the study of the thermal comfort and, implicitly, energy efficiency. The research is based on the analysis of the Internet of Things (IoT) literature, presenting a comparison among several approaches adopted. The central objective of the research is to outline the path that has been taken throughout the last decade towards a people-centric approach, discussing how users switched from being passive receivers of IoT services to being an active part of it. Basing on existing studies, authors performed what was a necessary and unprecedented grouping of the IoT applications to the thermal comfort into three categories: the thermal comfort studies with IoT hardware, in which the approach focuses on physical devices, the mimicking of IoT sensors and comfort using Building Simulation Models, based on the dynamic modelling of the thermal comfort through IoT systems, and Crowdsensing, a new concept in which people can express their sensation proactively using IoT devices. Analysing the trends of the three categories, the results showed that Crowdsensing has a promising future in the investigation through the IoT, although some technical steps forward are needed to achieve a satisfactory application to the thermal comfort matter.

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Section: Demonstration Studiesmentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

View full text Add to dashboard Cite

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“…Wireless sensors can be implemented with limited costs as compared to conventional wired sensors and the monitoring architecture can be adjusted dynamically in order to best capture field level information. In [7] a thermal comfort application using collected HVAC IoT data is presented. Building-level benchmarking data sets [8] are highly important to assess algorithm performance and produce reproducible outcomes.…”

Section: B Related Workmentioning

confidence: 99%

Data-Driven Modelling of Smart Building Ventilation Subsystem

et al. 2019

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Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations the application of conventional system identification or approximate greybox models, partly theoretic and partly data-driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

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“…Although there are many studies on NG demand forecasting, there are few studies that focus on predicting NG consumption in district heating systems at the city level. With the wide application of Internet of Things (IoT) technology [25], [26], the design of energy-saving monitoring systems for DHS is becoming increasingly perfect. The realization of a city-level heating monitoring system makes it possible to forecast the NG consumption of the whole city heating system.…”

Section: B Traditional Artificial Intelligence Modelsmentioning

confidence: 99%

Prediction of Natural Gas Consumption for City-Level DHS Based on Attention GRU: A Case Study for a Northern Chinese City

et al. 2019

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With the increasing energy demand and environmental protection requirements of heating systems, natural gas (NG), as an efficient and clean alternative energy source for coal, plays an increasingly important role in district heating systems (DHSs). The accurate prediction of the DHS consumption of NG in the whole city is helpful in formulating an efficient energy scheduling plan. However, because the DHS is a complex non-linear system with time-space delay effect and multi-factors, the traditional algorithms have deficiencies in the prediction accuracy of the natural gas consumption for city-level DHS. To achieve accurate prediction, a gas consumption prediction algorithm based on the attention gated recurrent unit (AGRU) model is proposed, which can obtain high-level features of historical data that affect gas consumption prediction. In addition, the attention mechanism can help to select more critical feature inputs and improve the prediction accuracy of gas consumption. Detailed comparative experiments were performed between the proposed AGRU and state-of-art NG consumption prediction algorithms, such as the recurrent neural network (RNN), long short-term memory (LSTM), GRU, attention RNN (ARNN), attention LSTM (ALSTM), support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR) and decision tree regression (DTR). An analysis of the experimental results shows that the proposed AGRU algorithm has a prediction accuracy of 95.3%, which is significantly better than other algorithms. In addition, the hyperparameters of the AGRU algorithm are also tested in detail to optimize the selection. INDEX TERMS Natural gas consumption prediction, district heating system, attention mechanism, gated recurrent unit.

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IoT-Based Smart Building Environment Service for Occupants’ Thermal Comfort

Cited by 48 publications

References 16 publications

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Data-Driven Modelling of Smart Building Ventilation Subsystem

Prediction of Natural Gas Consumption for City-Level DHS Based on Attention GRU: A Case Study for a Northern Chinese City

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