Article
Computer Science, Interdisciplinary Applications
Camilo J. Bastidas Pacheco, Joseph C. Brewer, Jeffery S. Horsburgh, Juan Caraballo
Summary: Developed CIWS is an open-source architecture to automate the process of collecting and managing high temporal resolution residential water use data. Through two case studies, it was tested for scalability and performance, proving to be reliable and effective. All elements of CIWS and the case study data are freely available for re-use.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Construction & Building Technology
L. Canale, T. Cholewa, G. Ficco, A. Siuta-Olcha, B. Di Pietra, P. Kolodziej, M. Dell'Isola
Summary: Within the residential sector, it is important to increase users' awareness and induce virtuous behaviors to reduce excessive uses of Domestic Hot Water (DHW) production. Individual metering and consumption-based billing have been found to be useful tools for reducing energy and water waste and increasing end-use energy efficiency. This study investigates the effect of individual metering systems on DHW consumption in the residential sector and finds that after the installation of individual DHW meters, there was a significant decrease in daily heat consumption and DHW volume withdrawn by buildings.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Chemistry, Analytical
Nour A. Attallah, Jeffery S. Horsburgh, Arle S. Beckwith, Robb J. Tracy
Summary: The datalogger presented is an open source device that can collect and analyze high temporal resolution residential water use data without disrupting the operation of existing analog residential water meters. By performing computation directly on the meter, it reduces data transmission requirements and latency, making it an efficient and effective edge computing solution.
Article
Environmental Sciences
Camilo J. Bastidas Pacheco, Jeffery S. Horsburgh, Arle S. Beckwith
Summary: This study investigates residential water end-use events using high temporal resolution water metering data. The results indicate that collecting data at full pulse resolution provides more accurate estimations of event features and facilitates easier and more accurate event classification.
Article
Automation & Control Systems
Wenqing Zhou, Bin Li, Hao Xiao, Hui Xiao, Wen Wang, Yingjun Zheng, Sheng Su
Summary: This study proposes a new method for detecting electricity theft users with zero electricity usage. By analyzing the correlation between water and electricity usage and utilizing multisource information, the proposed method can accurately identify these users more effectively.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Riccardo Zese, Elena Bellodi, Chiara Luciani, Stefano Alvisi
Summary: This study applies supervised Machine Learning techniques to automatically detect leaks of different magnitudes and compares the results with an empirical algorithm. The experimental results show that ML techniques significantly improve the detection performance of leaks.
Article
Environmental Sciences
Nora Gourmelon, Siming Bayer, Michael Mayle, Guy Bach, Christian Bebber, Christophe Munck, Christoph Sosna, Andreas Maier
Summary: This study aimed to quantitatively evaluate various machine learning techniques in water consumption behavior classification. The results showed that supervised methods are effective in classifying common residential end-uses, while unsupervised methods failed. Clustering techniques alone are not suitable for fully automatic separation of end-use categories.
Article
Energy & Fuels
Soumyajit Ghosh, Debashis Chatterjee
Summary: Smart meter technology is crucial in the context of smart grid connected residential load system, with non-intrusive load monitoring being a well-known method to assess power consumption and operating behavior of individual loads. The issue of identifying harmonic polluting loads has arisen due to modern household appliances injecting unwanted harmonics into the system. An improved technique using input aggregated voltage-current data and an Artificial Bee Colony algorithm has been proposed for load monitoring without heavy training mechanisms, showing promising results in comparison to state-of-the-art techniques.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Computer Science, Interdisciplinary Applications
Simona-Vasilica Oprea, Adela Bara
Summary: This paper proposes a signaling game model for optimization that utilizes advanced tariff schemes, smart meters, and smart appliances to optimize electricity consumption and reduce costs. Experimental results show that compared to traditional time-of-use tariffs, the use of advanced tariffs significantly improves savings and gains.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Environmental
Morgan Faye DiCarlo, Emily Zechman Berglund
Summary: This research explores customer compliance with water advisories using water consumption data collected through Advanced Metering Infrastructure (AMI). The study analyzes water use changes during a water main break and evaluates the volume of water saved through compliance with utility notifications. The results provide insights into the expected level and variation of water conservation during a Water Service Interruption (WSI).
Article
Water Resources
Diogo Fidelis Costa, A. K. Soares
Summary: Accurately determining water consumption is crucial for improving hydraulic modeling reliability. Smart metering systems can provide consumption data at a customer level and in small time intervals. This study focuses on a residential condominium in Brazil, where a smart metering system is implemented, to model consumers' water consumption and propose a system for detecting metering failures and internal water loss.
URBAN WATER JOURNAL
(2023)
Article
Engineering, Environmental
Morgan Faye DiCarlo, Emily Zechman Berglund
Summary: This research examines the impact of water advisories on consumer water conservation behaviors using data collected through Advanced Metering Infrastructure. The study found that water use behaviors changed during a major water service interruption, but different user segments responded differently to the water advisories. Statistical analysis showed that some consumers reduced their water usage in accordance with utility notifications and achieved water conservation goals.
Article
Environmental Sciences
Zhuangai Li, Xia Cao
Summary: This study compares the effectiveness of information feedback through emailing electricity bills and installing smart meters to promote electricity conservation, finding that providing information through bills can reduce household electricity consumption by around 20%, while feedback via smart meters installation has no positive effects due to lack of access and understanding. The study suggests that policymakers emphasize the importance of information feedback initiatives and improve the information feedback capacity of smart meters through training and education.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2021)
Article
Multidisciplinary Sciences
Netzah Calamaro, Moshe Donko, Doron Shmilovitz
Summary: This paper resolves a discrepancy between two smart-metering methods by providing mathematical formulations and experimental results. It also introduces an algebraic method suitable for energy metering and tariff computation.
Article
Physics, Multidisciplinary
Piotr Kiedrowski
Summary: By equipping smart meter devices with multiple communication modules, the elimination of the Transformer Station Data Concentrator module and the optimization of network architecture are achieved. The introduction of criteria for node selection and the use of graph theory allow for effective management of acquisition and intermediary nodes, enhancing the reliability and performance of the system.
Review
Engineering, Environmental
Zhiguo Yuan, Gustaf Olsson, Rachel Cardell-Oliver, Kim van Schagen, Angela Marchi, Ana Deletic, Christian Urich, Wolfgang Rauch, Yanchen Liu, Guangming Jiang
Article
Computer Science, Information Systems
Rachel Cardell-Oliver, Chayan Sarkar
ACM TRANSACTIONS ON SENSOR NETWORKS
(2019)
Article
Environmental Sciences
Ary Mazharuddin Shiddiqi, Rachel Cardell-Oliver, Amitava Datta
Article
Agriculture, Multidisciplinary
Omar Anwar, Adrian Keating, Rachel Cardell-Oliver, Amitava Datta, Gino Putrino
Summary: This study presents a multi-sensory, remote data acquisition system for beekeeping, which collects data from beehives and analyzes the impact of various environmental factors on hive weight changes. By evaluating different sensors and environmental variables, the system enhances decision making capability and contributes to the monitoring and management of beehives.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Economics
Rachel Cardell-Oliver, Doina Olaru
Summary: This paper proposes the CIAM model for classifying long-term passenger engagement with public transport. By evaluating a 5-year dataset, it identifies distinct patterns of long-term ridership and emphasizes the impact of passenger churn on ridership.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Computer Science, Artificial Intelligence
Omar Anwar, Adrian Keating, Rachel Cardell-Oliver, Amitava Datta, Gino Putrino
Summary: This work presents Apis-Prime, a hybrid deep learning model for soft sensing and time series forecasting, which estimates the daily weight variations of honeybee hives. It improves the state-of-the-art of the earlier proposed WE-Bee model and optimizes the beehive monitoring systems for weight variation estimation. The weight variations of a honeybee hive are crucial indicators of productivity and colony health.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Analytical
Weiyan Xu, Jack Sun, Rachel Cardell-Oliver, Ajmal Mian, Jin B. Hong
Summary: Smart metering systems (SMSs) are widely used for real-time tracking, outage notification, load forecasting, etc. However, the consumption data it generates can violate privacy. Homomorphic encryption (HE) offers privacy protection based on its security guarantees and computability over encrypted data. This paper proposes a privacy-preserving framework using HE with trust boundaries for different SMS scenarios, showing its feasibility in terms of performance and data privacy.
Proceedings Paper
Construction & Building Technology
Maira Alvi, Rachel Cardell-Oliver, Tim French
Summary: This paper proposes a method called AETL to enhance the performance of transfer learning in wastewater processing predictive models. The method leverages an autoencoder to generate large volumes of training data with a distribution similar to the target domain, and shows promising results on a real-world dataset.
PROCEEDINGS OF THE 2022 THE 9TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2022
(2022)
Article
Computer Science, Information Systems
Maira Alvi, Tim French, Rachel Cardell-Oliver, Philip Keymer, Andrew Ward
Summary: Wastewater treatment plants are complex systems that require monitoring using sensor systems. Soft sensor models can be a cost-effective alternative to expensive sensors for certain parameters in wastewater. This paper proposes a hybrid neural network architecture for learning soft sensors for complex phenomena, and validates the effectiveness using real-world data from a wastewater treatment plant. Additionally, a annotated dataset of a secondary wastewater treatment plant is publicly released to accelerate research in the development of soft sensors.
Review
Water Resources
Richard Koech, Rachel Cardell-Oliver, Geoff Syme
Summary: The adoption of smart water metering technology has been increasing in recent decades, primarily driven by engineering and efficiency considerations. As the technology matures and costs decrease, the adoption rate of smart water meters is expected to rise. Regulatory and social factors will also play a role in shaping the adoption of this technology.
AUSTRALASIAN JOURNAL OF WATER RESOURCES
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Rachel Cardell-Oliver, Prathyusha Sangam
BUILDSYS'19: PROCEEDINGS OF THE 6TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION
(2019)
Proceedings Paper
Computer Science, Software Engineering
Rachel Cardell-Oliver, Christof Huebner, Matthias Leopold, Jason Beringer
DATA'19: PROCEEDINGS OF THE SECOND ACM WORKSHOP ON DATA ACQUISITION TO ANALYSIS
(2019)
Proceedings Paper
Computer Science, Hardware & Architecture
Benjamin Dix-Matthews, Rachel Cardell-Oliver, Christof Huebner
PROCEEDINGS OF THE 7TH INTERNATIONAL WORKSHOP ON REAL-WORLD EMBEDDED WIRELESS SYSTEMS AND NETWORKS (REALWSN'18)
(2018)
Proceedings Paper
Computer Science, Information Systems
Michael Stewart, Wei Liu, Rachel Cardell-Oliver, Rui Wang
2018 9TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK)
(2018)
Proceedings Paper
Computer Science, Information Systems
Rachel Cardell-Oliver, Travis Povey
BUILDSYS'18: PROCEEDINGS OF THE 5TH CONFERENCE ON SYSTEMS FOR BUILT ENVIRONMENTS
(2018)