scholarly journals Capturing high-resolution water demand data in commercial buildings

2021 ◽  
Author(s):  
Peter Melville-Shreeve ◽  
Sarah Cotterill ◽  
David Butler
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Water Demand ◽  
Rainwater Harvesting ◽  
Service Providers ◽  
Low Cost ◽  
Time Data ◽  
Smart Meters ◽  
High Resolution Data ◽  
Site Water

Abstract Water demand measurements have historically been conducted manually, from meter readings less than once per month. Leading water service providers have begun to deploy smart meters to collect high-resolution data. A low-cost flush counter was developed and connected to a real-time monitoring platform for 119 ultra-low flush toilets in 7 buildings on a university campus to explore how building users influence water demand. Toilet use followed a typical weekly pattern in which weekday use was 92% ± 4 higher than weekend use. Toilet demand was higher during term time and showed a strong, positive relationship with the number of building occupants. Mixed-use buildings tended to have greater variation in toilet use between term time and holidays than office-use buildings. The findings suggest that the flush sensor methodology is a reliable method for further consideration. Supplementary data from the study's datasets will enable practitioners to use captured data for (i) forecast models to inform water resource plans; (ii) alarm systems to automate maintenance scheduling; (iii) dynamic cleaning schedules; (iv) monitoring of building usage rates; (v) design of smart rainwater harvesting to meet demand from real-time data; and (vi) exploring dynamic water pricing models, to incentivise optimal on-site water storage strategies.

scholarly journals Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Omar Isaac Asensio ◽  
M. Cade Lawson ◽  
Camila Z. Apablaza
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Electric Vehicle ◽  
Optimal Power Flow ◽  
Service Providers ◽  
High Temporal Resolution ◽  
High Resolution Data ◽  
Demand Models ◽  
Charging Stations ◽  
Resolution Data

AbstractProblems of poor network interoperability in electric vehicle (EV) infrastructure, where data about real-time usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of real-time EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and free sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.

scholarly journals New Data Buoys Watch Typhoons from Within the Storm

Eos ◽  
2017 ◽  
Author(s):  
Sen Jan ◽  
Yiing Yang ◽  
Hung-I Chang ◽  
Ming-Huei Chang ◽  
Ching-Ling Wei
Keyword(s):  
High Resolution ◽  
Numerical Model ◽  
Pacific Ocean ◽  
Real Time ◽  
Northwestern Pacific ◽  
Northwestern Pacific Ocean ◽  
Time Data ◽  
High Resolution Data ◽  
Real Time Data ◽  
Resolution Data

Advanced real-time data buoys have observed nine strong typhoons in the northwestern Pacific Ocean since 2015, providing high-resolution data and reducing the uncertainty of numerical model forecasts.

scholarly journals Development and Calibration of an Open Source, Low-Cost Power Smart Meter Prototype for PV Household-Prosumers

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 878 ◽  
Author(s):  
Sanchez-Sutil ◽  
Cano-Ortega ◽  
Hernandez ◽  
Rus-Casas
Keyword(s):  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Easy Access ◽  
International Electrotechnical Commission ◽  
Smart Meter ◽  
Smart Meters ◽  
Accuracy Class ◽  
Real Time Processing ◽  
High Resolution Data

Smart meter roll-out in photovoltaic (PV) household-prosumers provides easy access to granular meter measurements, which enables advanced energy services. The design of these services is based on the training and validation of models. However, this requires temporal high-resolution data for generation/load profiles collected in real-world household facilities. For this purpose, this research developed and successfully calibrated a new prototype for an accurate low-cost On-time Single-Phase Power Smart Meter (OSPPSM), which corresponded to these profiles. This OSPPSM is based on the Arduino open-source electronic platform. Not only can it locally store information, but can also wirelessly send these data to cloud storage in real-time. This paper describes the hardware and software design and its implementation. The experimental results are presented and discussed. The OSPPSM demonstrated that it was capable of in situ real-time processing. Moreover, the OSPPSM was able to meet all of the calibration standard tests in terms of accuracy class 1 (measurement error ≤1%) included in the International Electrotechnical Commission (IEC) standards for smart meters. In addition, the evaluation of the uncertainty of electrical variables is provided within the context of the law of propagation of uncertainty. The approximate cost of the prototype was 60 € from eBay stores.

Complement C3 Genotyping of Slow and Fast Variants by Real Time PCR-High Resolution Melting

2012 ◽  
Vol 10 (3) ◽  
pp. 329-334 ◽  
Author(s):  
D.M. Valero-Hervás ◽  
P. Morales ◽  
M.J. Castro ◽  
P. Varela ◽  
M. Castillo-Rama ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Real Time Pcr ◽  
High Resolution Melting ◽  
Low Cost ◽  
Complement C3 ◽  
Amplification Refractory Mutation System ◽  
Rt Pcr ◽  
Dna Amount ◽  
Mutation System

“Slow” and “Fast” C3 complement variants (C3S and C3F) result from a g.304C>G polymorphism that changes arginine to glycine at position 102. C3 variants are associated with complement-mediated diseases and outcome in transplantation. In this work C3 genotyping is achieved by a Real Time PCR - High Resolution Melting (RT-PCR-HRM) optimized method. In an analysis of 49 subjects, 10.2% were C3FF, 36.7% were C3SF and 53.1% were C3SS. Allelic frequencies (70% for C3S and 30% for C3F) were in Hardy-Weinberg equilibrium and similar to those published previously. When comparing RT-PCR-HRM with the currently used Tetraprimer-Amplification Refractory Mutation System PCR (T-ARMS-PCR), coincidence was 93.8%. The procedure shown here includes a single primer pair and low DNA amount per reaction. Detection of C3 variants by RT-PCR-HRM is accurate, easy, fast and low cost, and it may be the method of choice for C3 genotyping.

scholarly journals Design of an IoT based Real Time Environment Monitoring System using Legacy Sensors

2018 ◽  
Vol 210 ◽  
pp. 03008
Author(s):  
Aparajita Das ◽  
Manash Pratim Sarma ◽  
Kandarpa Kumar Sarma ◽  
Nikos Mastorakis
Keyword(s):  
Air Pollution ◽  
Light Intensity ◽  
Real Time ◽  
Environmental Conditions ◽  
Low Cost ◽  
Environment Monitoring ◽  
Time Data ◽  
Graphical Information ◽  
Real Time Data ◽  
Sun Light

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.

New Intelligent Push-the-Bit Rotary Steerable System Helped Reducing Well Time and Maximized Directional Drilling Performance, Abu Dhabi, UAE

2021 ◽  
Author(s):  
Saif Al Arfi ◽  
Fatima AlSowaidi ◽  
Fernando Ruiz ◽  
Ibrahim Hamdy ◽  
Yousef Tobji ◽  
...  
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Service Providers ◽  
Directional Drilling ◽  
Cruise Control ◽  
Time Data ◽  
Human Errors ◽  
Drilling Performance ◽  
Oil And Gas Fields ◽  
Gas Market

Abstract To meet the current oil and gas market challenges, there is an industry need to optimize cost by safely drilling longer horizontal wells to maximize well productivity. Drilling challenges include the highly deviated trajectory that starts from the surface sections and wellhead, the high DogLeg Sevirity (DLS) profile with collision risks, and the thin complex geological structures, especially in new unconventional fields where numerous geological and geomechanical uncertainties are present. To mitigate for those challenges, reviewing the existing drilling techniques and technologies is necessary. To compete in the current Hi-Tech and Automation era, the main challenges for directional drilling service providers are to reduce well time, place wells accurately, and improve reliability, reducing repair and maintenance costs and helping the customer reduce time and costs for the overall project. Offset wells analysis and risk assessments allowed identifying the main challenges and problems during directional drilling phases, which were highlighted and summarized. As a proposed solution, the new generation of intelligent fully rotating high dogleg push-the-bit rotary steerable system has been implemented in the UAE onshore oil and gas fields to improve the directional drilling control and the performance. This implementation reduced the Non-Productive time (NPT) related to the human errors as the fully automation capabilities were being utilized. The new rotary steerable system has the highest mechanical specs in the market including self-diagnosis and self-prognosis through digital electronics and sophisticated algorithms that monitor equipment health in real-time and allow for managing the tool remotely. As a result, the new intelligent RSS was implemented in all possible complex wellbore conditions, such as wells with high DLS profile, drilling vertical, curve, and lateral sections in a single trip with high mud weight and high solid contents. Automation cruise control gave the opportunity to eliminate any well profile issues and maintain the aggressive drilling parameters. Using the Precise Near-bit Inclination and Azimuth and the At-Bit Gamma real-time data and high-frequency tool face measurements in the landing intervals where required for precise positional control to enable entering the reservoir in the correct location and with the correct attitude helping the customer's Geology and Geophysics department to place wells accurately while maintaining a high on bottom ROP.

scholarly journals ACCESS TO DIGITAL CAR DATA AND COMPETITION IN AFTERMARKET MAINTENANCE SERVICES

2020 ◽  
Vol 16 (1) ◽  
pp. 116-141
Author(s):  
Bertin Martens ◽  
Frank Mueller-Langer
Keyword(s):  
Real Time ◽  
Network Effects ◽  
Economies Of Scale ◽  
Service Providers ◽  
Data Access ◽  
Third Party ◽  
Similar Data ◽  
Time Data ◽  
Price Discriminate ◽  
Data Portability

Abstract Before the arrival of digital car data, car manufacturers had already partly foreclosed the maintenance market through franchising contracts with a network of exclusive official dealers. EU regulation endorsed this foreclosure but mandated access to maintenance data for independent service providers to keep competition in these markets. The arrival of digital car data upsets this balance because manufacturers can collect real-time maintenance data on their servers and send messages to drivers. These can be used to price discriminate and increase the market share of official dealers. There are at least four alternative technical gateways that could give independent service providers similar data access options. However, they suffer in various degrees from data portability issues, switching costs and weak network effects, and insufficient economies of scale and scope in data analytics. Multisided third-party consumer media platforms appear to be better placed to overcome these economic hurdles, provided that an operational real-time data portability regime could be established.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

Adopting Citizen Observations in Operational Weather Prediction

2019 ◽  
Vol 101 (1) ◽  
pp. E43-E57 ◽  
Author(s):  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Cristian Lussana ◽  
Jørn Kristiansen ◽  
Øystein Hov
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Weather Prediction ◽  
Urban Heat Islands ◽  
Production Chain ◽  
Near Surface ◽  
High Resolution Data ◽  
Baltic Countries ◽  
Operational Systems ◽  
Weather Stations

Abstract Citizen weather stations are rapidly increasing in prevalence and are becoming an emerging source of weather information. These low-cost consumer-grade devices provide observations in real time and form parts of dense networks that capture high-resolution meteorological information. Despite these benefits, their adoption into operational weather prediction systems has been slow. However, MET Norway recently introduced observations from Netatmo’s network of weather stations in the postprocessing of near-surface temperature forecasts for Scandinavia, Finland, and the Baltic countries. The observations are used to continually correct errors in the weather model output caused by unresolved features such as cold pools, inversions, urban heat islands, and an intricate coastline. Corrected forecasts are issued every hour. Integrating citizen observations into operational systems comes with a number of challenges. First, operational systems must be robust and therefore rely on strict quality control procedures to filter out unreliable measurements. Second, postprocessing methods must be selected and tuned to make use of the high-resolution data that at times can contain conflicting information. Central to resolving these challenges is the need to use the massive redundancy of citizen observations, with up to dozens of observations per square kilometer, and treating the data source as a network rather than a collection of individual stations. We present our experiences with introducing citizen observations into the operational production chain of automated public weather forecasts. Their inclusion shows a clear improvement to the accuracy of short-term temperature forecasts, especially in areas where existing professional stations are sparse.

scholarly journals Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

2020 ◽  
Vol 10 (20) ◽  
pp. 7054 ◽  
Author(s):  
Muzaffar Rao ◽  
Liam Lynch ◽  
James Coady ◽  
Daniel Toal ◽  
Thomas Newe
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Low Cost ◽  
Intelligent Vehicles ◽  
Current Status ◽  
Main Task ◽  
Time Data ◽  
Manufacturing Execution Systems ◽  
Product Delivery ◽  
Real Time Information

Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik ‘RB-1′ robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV.

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