Optimisation of the data point configurations in a building environmental monitoring system

Faridah, N.A.; Sunarno, N.A.; Utami, Sentagi Sesotya; Nurjani, Emilya; Hanif, Muhammad Ilham; Waruwu, Memory Motivanisman; Wijaya, Rony

doi:10.1504/ijcnds.2021.118121

Cited by 4 publications

(5 citation statements)

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“…The system uses the TMS320DM6446 designed by Da Vinci of TI company as the image processor. Its role is to carry out in-depth analysis, processing, storage and transmission of the digital video signal transmitted from the video acquisition module, and can also cooperate with the video differential algorithm in the ARM call DSP, encode and compress the parsed data, and save it on the hard disk [11]. The memory part consists of two parts, memory connected to the DDR2 controller and SRAM and NOR/NANDFlash connected to the EMIFA.…”

Section: Video Processing Modulementioning

confidence: 99%

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

Kaipeng Zhang, Jingxu Zuo

2024

jes

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The author proposes an intelligent early-warning system for preventing external damage of transmission lines. The system uses TI-TMS320DM6446 as the core, the peripheral circuit configuration and video monitoring; The moving objects captured by PTZ camera are detected and identified by using mixed Gaussian model, background difference and minimum tangent matrix, and alarm signals are provided. According to the particularity of the power supply of the equipment on the transmission line, the basic power management of the system is analyzed in detail. A reliable power management scheme is designed. The practical application proves that this method has good real-time performance and has obvious effect on preventing power cable damage by force.

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Section: Video Processing Modulementioning

confidence: 99%

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

Kaipeng Zhang, Jingxu Zuo

2024

jes

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“…Data generated by BEMS plays a crucial role in comprehending the characteristics of building systems and acquiring appropriate strategies to achieve the desired performance. 25 Numerous studies have been conducted to address challenges related to indoor environmental quality (IEQ) and energy efficiency in buildings. [26][27][28] BEMS monitors variables related to IEQ, such as thermal comfort, 29 visual performance, 30 and air quality.…”

Section: Introductionmentioning

confidence: 99%

An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Faridah,

Utami,

Wijaya

et al. 2024

Building Services Engineering Research and Technology

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Indoor air quality is the foundation of a good indoor environment. The COVID-19 pandemic further highlighted the importance of providing real-time airflow distribution information within the Building Environmental Monitoring System (BEMS) to minimize the risk of infectious airborne transmission. This paper discusses the process of developing a predictive model for indoor airflow distribution prediction with indoor and outdoor input parameters using machine learning and its implementation in healthy BEMS for a classroom in the tropical climate region of Yogyakarta, Indonesia. This paper encompassed field measurement and simulation involving outdoor climate conditions and the operational status of the classroom’s windows, Air Conditioning units, and fans. Three machine learning models were constructed using OLS, LASSO, and Ridge methods. Datasets for the modeling were generated from CFD model simulations in IES VE and were assessed for correlation. The mean temperature and velocity differences between the CFD model simulation and measurement results are 0.21°C and 0.083 m/s, respectively. Outdoor climate conditions and the operational status of the classroom’s utilities significantly influence the indoor airflow distribution characteristics. The three models indicate a relatively poor performance, where the classroom had a relatively low sensitivity to input changes. However, the best model performance was achieved using the LASSO method, with average values from post-normalization of [Formula: see text] and Root Mean Square Error (RMSE) of 0.336 and 0.077, respectively. The model was implemented in healthy BEMS on the “Platform for Healthy and Energy Efficient Building Management System.” Practical Application: This research proposed a machine learning model of indoor airflow characteristics of a classroom in Yogyakarta. The proposed model can be adapted to produce monitoring systems that best represent the related conditions. The method can be adopted to develop a relatively simple, low-cost sensor or model to monitor an indoor environment. Future studies may explore the results of the real-world implementation in a case study.

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“…Net Zero Energy Building (NZEB) is one of the innovative concepts for sustainable buildings because of its ability to save energy and reduce carbon emissions simultaneously [1]. The concept of the NZEB itself is based on the principle that buildings can meet their energy needs from cheap, locally available, non-polluting and renewable sources [2]. This is because net zero design is closely related to optimizing building designs so as to minimize energy use as low as possible so that excess renewable energy can be returned to the building's electricity network.…”

Section: Introductionmentioning

confidence: 99%

“…NZEB achievements can also be obtained by using energy-efficient materials and building envelope designs, then applying energy generation systems with sustainable and renewable sources (EBT) [8]. According to Utami, et al [2] renewable supply side energy technologies that can be used in the NZEB context include the use of photovoltaics, solar hot water, wind, hydropower, and biofuels.…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Tall Building Energy Performance by Using EDGE Application towards Net Zero Energy Building (NZEB)

Hajji,

Virginia,

Ariestadi

2023

E3S Web Conf.

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Net Zero Energy Building (NZEB) is one of the innovative concepts for sustainable buildings because of its ability to save energy and reduce carbon emissions simultaneously. This study used a descriptive approach to review tall building data through the parameter of saving electricity by using the EDGE application. Primary data is data obtained from working drawings or detailed engineering design (DED), work plans and specifications, and budget plans. EDGE application also produced several outputs to analyze energy performance: final energy use, operational CO2 emissions, embodied energy, utility cost, cooling loads, energy savings, operational CO2 savings, cost savings, annual EPI base case, improved EPI, number of affected people, base case and improved global warming potential of refrigerants. The EDGE also analyzed the following data: building type, site location, project details, building utility data, occupational and operational information, building dimension and specification, HVAC system, fuel used, and climate data. The energy performance of the building is analyzed by using EDGE’s parameters coded by EEM 01-37 that span from the windows-to-walls ratio to the use of low environmental impact refrigerant.

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Optimisation of the data point configurations in a building environmental monitoring system

Cited by 4 publications

References 0 publications

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

The Analysis of Tall Building Energy Performance by Using EDGE Application towards Net Zero Energy Building (NZEB)

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