scholarly journals A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3591 ◽  
Author(s):  
Junqi Wang ◽  
Norman Tse ◽  
Tin Poon ◽  
John Chan
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Real Life ◽  
Demand Estimation ◽  
Energy Performance ◽  
Operating Efficiency ◽  
Computationally Efficient ◽  
Existing Building ◽  
Building Management ◽  
Indoor And Outdoor

The operating efficiency of heating, ventilation and air conditioning (HVAC) system is critical for building energy performance. Demand-based control is an efficient HVAC operating strategy, which can provide an appropriate level of HVAC services based on the recognition of actual cooling “demand.” The cooling demand primarily relies on the accurate detection of occupancy. The current researches of demand-based HVAC control tend to detect the occupant count using cameras or other sensors, which often impose high computation and costs with limited real-life applications. Instead of detecting the occupant count, this paper proposes to detect the occupancy density. The occupancy density (estimated by image foreground moving pixels) together with the indoor and outdoor information (acquired from existing sensors) are used as inputs to an artificial neural network model for cooling demand estimation. Experiments have been implemented in a university design studio. Results show that, by adding the occupancy density, the cooling demand estimation error is greatly reduced by 67.4% and the R value is improved from 0.75 to 0.96. The proposed approach also features low-cost, computationally efficient, privacy-friendly and easily implementable. It shows good application potentials and can be readily incorporated into existing building management systems for improving energy efficiency.

scholarly journals Application of IoT and BEMS to Visualise the Environmental Performance of an Educational Building

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4009
Author(s):  
Mohataz Hossain ◽  
Zhenzhou Weng ◽  
Rosa Schiano-Phan ◽  
David Scott ◽  
Benson Lau
Keyword(s):  
Real Time ◽  
Environmental Performance ◽  
Learning Experience ◽  
Low Cost ◽  
Environmental Parameters ◽  
Energy Performance ◽  
Existing Building ◽  
Building Energy Management ◽  
Sound Levels ◽  
Questionnaire Surveys

This paper presents the application of Internet of Things (IoT) Technology and Building Energy Management System (BEMS) within the Marylebone Campus of the University of Westminster, located in central London, to improve the environmental performance of the existing building as well as enhance the learning experience on energy and sustainability. Sixty IoT sensors connected to minicomputers were planned to be deployed within three floors of the building to continuously measure the real-time environmental parameters, such as dry-bulb temperature, relative humidity, illuminance level, carbon dioxide, and sound levels. Experimental workshops were also arranged with undergraduate and post-graduate students at their classrooms using IoT sensors, portable Bluetooth sensors and online questionnaires to increase awareness of the effect of environmental and behavioural changes on energy saving through real-time visualisation. Users’ subjective feedback on their workplace was also collected through Post Occupancy Evaluation (POE) questionnaire surveys. The results show the effectiveness of IoT systems and BEMS in supplying the building users and management with high-resolution, low-cost data acquisition systems highlighting the existing challenges and future scopes. The study also documents the process and the improvement in students’ awareness of environmental and energy performance of their building through IoT data visualizations and POE.

scholarly journals Overview of the Available Knowledge for the Data Model Definition of a Building Renovation Passport for Non-Residential Buildings: The ALDREN Project Experience

2020 ◽  
Vol 12 (2) ◽  
pp. 642 ◽  
Author(s):  
Marta Maria Sesana ◽  
Mathieu Rivallain ◽  
Graziano Salvalai
Keyword(s):  
Data Model ◽  
Residential Buildings ◽  
Low Cost ◽  
Co2 Reduction ◽  
Energy Performance ◽  
Building Stock ◽  
Existing Building ◽  
Data Links ◽  
The One ◽  
High Level

According to its strategic long-term vision, Europe wants to be a climate-neutral economy by 2050. Buildings play a crucial role in this vision, and they represent a sector with low-cost opportunities for high-level CO2 reduction. The challenge the renovation of the existing building stock, which must be increased to 3%/year, more than double compared to the current 1.2%/year. In this context, the ALliance for Deep RENovation (ALDREN) project has the goal of encouraging property owners to undertake renovation of existing buildings using a clear, robust, and comparable method. This paper aims to present the ALDREN approach and the ALDREN Building Renovation Passport (BRP), giving an overview of the connections and data links to other existing databases and certification schemes. To understand the data value potential of buildings, one requires reliable and trustworthy information. The Building Renovation Passport, introduced by the recent Energy Performance Building Directive (EPBD) recast 844/2018/EU, aims to provide this information. This paper presents the experience of the ALDREN BRP for non-residential buildings as well as the development procedure for its data model and the potential that this tool could have for the construction market. The ALDREN BRP has been structured into two main parts—BuildLog and RenoMap—with a common language that facilitates communication on the one hand and, on the other, the setting of renovation targets based on lifetime, operation, and user needs.

scholarly journals Smart Bluetooth beacon based on LoRa Positioning method and system research

2021 ◽  
Vol 2079 (1) ◽  
pp. 012032
Author(s):  
Rui Su ◽  
Yawen Dai
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Real Life ◽  
Signal Attenuation ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
New Type ◽  
Positioning Analysis ◽  
Transmission Speed ◽  
Indoor And Outdoor

Abstract In the era of the Internet of Everything, applications based on real-time location continue to appear in various industries. The indoor and outdoor positioning, analysis and management of personnel and objects can effectively improve the efficiency of production and management, which is of great significance to many industries. The use of Bluetooth beacon positioning has the advantages of low energy consumption, low cost, and fast data transmission speed. However, in real life, there are two obstacles to receiving signals, which are easily affected by the environment and the need for frequent on-site maintenance. This paper designs and implements a new type of LoRa-based smart Bluetooth beacon, which can be quickly connected to LoRa. Online monitoring and remote control can achieve better adaptation to the environment, and can fit a better signal attenuation model in the deployment environment in real time. Traditional signal strength positioning solutions have their own limitations. In order to improve the positioning accuracy of the Bluetooth beacon and the universality of the algorithm, in view of the low deployment density of beacons, the positioning accuracy is not good and the anchor circle has various situations, an optimized weighted centroid positioning scheme integrating greedy strategy is proposed.

PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  
Keyword(s):  
Open Source ◽  
Surveillance System ◽  
Community Care ◽  
Communicable Disease ◽  
Low Cost ◽  
Real Life ◽  
Disease Outbreak ◽  
Disease Spread ◽  
Healthcare Team ◽  
Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

scholarly journals Minimal Monitoring of Improvements in Energy Performance after Envelope Renovation in Subsidized Single Family Housing in Madrid

2020 ◽  
Vol 13 (1) ◽  
pp. 235
Author(s):  
Fernando Martín-Consuegra ◽  
Fernando de Frutos ◽  
Ignacio Oteiza ◽  
Carmen Alonso ◽  
Borja Frutos
Keyword(s):  
Energy Efficiency ◽  
Energy Savings ◽  
Low Cost ◽  
Energy Performance ◽  
Quality Parameters ◽  
Urban Setting ◽  
Single Family ◽  
Nineteen Fifties ◽  
Environment Quality ◽  
Urban Complex

This study quantified the improvement in energy efficiency following passive renovation of the thermal envelope in highly inefficient residential complexes on the outskirts of the city of Madrid. A case study was conducted of a single-family terrace housing, representative of the smallest size subsidized dwellings built in Spain for workers in the nineteen fifties and sixties. Two units of similar characteristics, one in its original state and the other renovated, were analyzed in detail against their urban setting with an experimental method proposed hereunder for simplified, minimal monitoring. The dwellings were compared on the grounds of indoor environment quality parameters recorded over a period covering both winter and summer months. That information was supplemented with an analysis of the energy consumption metered. The result was a low-cost, reasonably accurate measure of the improvements gained in the renovated unit. The monitoring output data were entered in a theoretical energy efficiency model for the entire neighborhood to obtain an estimate of the potential for energy savings if the entire urban complex were renovated.

scholarly journals BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings

2021 ◽  
Vol 13 (8) ◽  
pp. 4496
Author(s):  
Giuseppe Desogus ◽  
Emanuela Quaquero ◽  
Giulia Rubiu ◽  
Gianluca Gatto ◽  
Cristian Perra
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Application Programming Interface ◽  
Visual Programming ◽  
Information Modeling ◽  
Sensor Data ◽  
Time Data ◽  
Existing Building ◽  
Building Maintenance ◽  
Indoor Conditions

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

Measuring battery discharge characteristics for accurate UAV endurance estimation

2020 ◽  
Vol 124 (1277) ◽  
pp. 1099-1113
Author(s):  
L. Mariga ◽  
I. Silva Tiburcio ◽  
C.A. Martins ◽  
A.N. Almeida Prado ◽  
C. Nascimento
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Flight Time ◽  
Time Estimates ◽  
Discharge Rates ◽  
Nonlinear Optimisation ◽  
Time Calculation ◽  
Discharge Profile ◽  
Battery Discharge ◽  
Battery Discharge Characteristics

ABSTRACTThe increasing use of unmanned aerial vehicles in areas such as rescue, mapping, and transportation have made it necessary to study more accurate techniques for calculating flight time estimates. Such calculations require knowing the battery discharge profile. Simplified flight time calculation methods provide data with uncertainties as they are based solely on manufacturer datasheet information. This study presents a setup to measure the battery discharge curve using a LabVIEW interface with a low-cost acquisition system. The acquired data passes through a nonlinear optimisation algorithm to find the battery coefficients, which enables the more precise estimation of its range and endurance. The great advantage of this model is that it makes it possible to predict how the battery will discharge at different rates using just one experimental curve. The methodology was applied to three different batteries and the model was validated with different discharge rates in a controlled environment, which resulted in endurance lower than 3.0% for most conditions and voltage estimation error lower than 3.0% in operational voltage. The work also presented a methodology for estimating cruise time based on the current used during each flight stage.

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