Article
Engineering, Electrical & Electronic
Inam Ullah Khan, Nadeem Javaid, C. James Taylor, Xiandong Ma
Summary: The role of electricity theft detection is crucial in maintaining cost-efficiency in smart grids. Existing methods are limited by the large volume of data, missing values, and non-linearity. A novel framework is proposed that combines three modules to address these issues. The framework efficiently handles missing values, imbalanced datasets, and accurately predicts electricity theft cases using a hybrid classification approach and an improved artificial neural network.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Construction & Building Technology
Laszlo Czetany, Viktoria Vamos, Miklos Horvath, Zsuzsa Szalay, Adrian Mota-Babiloni, Zsofia Deme-Belafi, Tamas Csoknyai
Summary: This research assessed a high-resolution electric load dataset from nearly a thousand households in Hungary, using different clustering methods and validity indexes to identify energy consumption profiles. The k means clustering technique was found to be the best method, and analyses were conducted to identify different consumer groups and parameters affecting energy consumption.
ENERGY AND BUILDINGS
(2021)
Article
Engineering, Multidisciplinary
Wenlong Liao, Zhe Yang, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Leandro Von Krannichfeldt, Yusen Wang, Dechang Yang
Summary: In this article, simple data augmentation tricks (SDAT) are proposed to address the data imbalance issue in electricity theft detection. Five simple but powerful operations are introduced, including adding noises, drifting values, quantizing readings, adding fixed and changeable values. Numerical simulations on a real-world dataset demonstrate that SDAT can significantly improve the performance of different classifiers, particularly for small datasets. Specific suggestions on parameter selection for SDAT's transferability to other datasets are also provided.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Economics
Fissha Asmare, Jurate Jaraite, Andrius Kazukauskas
Summary: Research suggests that providing descriptive hourly electricity information on web portals can decrease residential electricity consumption by approximately 8.6%. This effect is especially noticeable for households with high energy consumption, living in rural areas, and residing in detached houses.
Article
Multidisciplinary Sciences
Lucas Pereira, Donovan Costa, Miguel Ribeiro
Summary: Smart meter data is crucial for next-generation power grids and machine learning algorithms. The SustDataED2 dataset described in this paper provides aggregated and individual appliance consumption data, as well as timestamps for evaluating machine learning problems.
Article
Economics
Victor von Loessl
Summary: Data privacy concerns are a major reason why households are reluctant to have smart meters, which are crucial for future energy systems. A household survey in Germany reveals that privacy concerns are negatively associated with trust and technological affinity. However, high levels of energy-related financial literacy and environmental values can lead to either low or high privacy concerns. These concerns significantly impact households' aversion to dynamic pricing, with additional information about data handling reducing aversion for those with high privacy concerns.
Article
Engineering, Electrical & Electronic
Yazhou Jiang
Summary: This study proposes a data-driven probabilistic fault location methodology based on comprehensive sensing measurement from digital relays at substations, IEDs along primary feeders, SCADA sensors in the feeder circuit, and smart meters at customers' premises. Historical fault location accuracies by digital relays and IEDs are used to estimate fault location errors in real time with probability. Multiple-hypothesis analysis is implemented to handle uncertainties from SCADA sensors and smart meters, providing system operators with a list of potential fault locations for decision-making. Simulation results validate the efficacy of the proposed approach for fault diagnosis.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Energy & Fuels
Ejaz Ul Haq, Can Pei, Ruihong Zhang, Huang Jianjun, Fiaz Ahmad
Summary: Electricity theft has a significant negative impact on energy suppliers and power infrastructure, resulting in non-technical losses and business losses. Smart grids can help address the issue of power theft by integrating information and energy flow, and the analysis of smart grid data aids in the detection of power theft. This study proposes an electricity theft detection approach using smart meter consumption data to mitigate the aforementioned challenges and assist in assessing energy supply businesses in managing limited energy, unexpected power usage, and poor power management.
Review
Environmental Studies
Harrison Hampton, Aoife Foley, Dylan Furszyfer Del Rio, Beatrice Smyth, David Laverty, Brian Caulfield
Summary: This study reviews and summarizes the customer engagement trends in retail electricity markets, and proposes the policy recommendation of integrating market mechanisms and technology to ensure the operation of retail electricity markets and achieve a carbon-neutral society. The study also suggests future research directions to promote customer engagement in retail electricity market design.
ENERGY RESEARCH & SOCIAL SCIENCE
(2022)
Article
Construction & Building Technology
Farhang Raymand, Behzad Najafi, Alireza Haghighat Mamaghani, Amin Moazami, Fabio Rinaldi
Summary: Smart meter-driven remote auditing allows rapid identification of buildings with low energy performance. This study focuses on using ML-based pipelines to characterize buildings and optimize their performance using electrical and chilled-water consumption data. Results show that optimizing the pipelines improves model accuracy and interpretability, and adding features from chilled-water consumption data further enhances accuracy and reduces feature count.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Hidenori Komatsu, Osamu Kimura
Summary: Among the techniques supporting energy conservation, clustering has been widely used for customer segmentation. However, the potential of clustering techniques based on smart meter data analytics for small- and medium-sized enterprises to recognize building types has not been sufficiently investigated. This study evaluated two methods for clustering based on an open smart meter dataset and found that both methods were reasonable in estimating building types.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Florencia Lazzari, Gerard Mor, Jordi Cipriano, Eloi Gabaldon, Benedetto Grillone, Daniel Chemisana, Francesc Solsona
Summary: This paper presents a novel approach to forecast day-ahead electricity consumption for residential households, taking into account highly irregular human behavior. The methodology uses machine-learning techniques to handle missing data and outliers, and improves the forecasting of individual customer's electricity consumption by identifying and predicting user behavior.
Article
Construction & Building Technology
Pedram Nojedehi, Burak Gunay, William O'Brien
Summary: This paper proposes a method that integrates occupants' feedback and personal data to enhance the performance of fault detection and diagnostic (FDD) technology in buildings. The method collects occupants' subjective feedback on thermal comfort and air quality using a smartwatch app called Cozie, and retrieves sensor readings from the building automation system. FDD rulesets then use the data to determine process variable errors and report alarms. The results show that occupant input accurately proved/disproved alarms and allowed for a faster and more accurate FDD.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Martina Ferrando, Alessia Banfi, Francesco Causone
Summary: The COVID-19 pandemic has had a significant impact on residential electricity usage, leading to an increase in electricity consumption during the lockdown period and a shift in usage patterns.
SUSTAINABLE CITIES AND SOCIETY
(2023)
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
Energy & Fuels
Hidenori Komatsu, Ken-ichiro Nishio
Article
Multidisciplinary Sciences
Hidenori Komatsu, Hiromi Kubota, Nobuyuki Tanaka, Hirotada Ohashi
Article
Construction & Building Technology
Hidenori Komatsu, Osamu Kimura
ENERGY AND BUILDINGS
(2020)
Article
Multidisciplinary Sciences
Hidenori Komatsu, Hiromi Kubota, Nobuyuki Tanaka, Mariah Griffin, Jennifer Link, Glenn Geher, Maryanne L. Fisher
Summary: Considering the trade-off between risks and benefits, proper recycling and limiting the use of disposable plastics can improve social welfare. A sense of familial support positively contributes to the effectiveness of information provision.
Article
Multidisciplinary Sciences
Hidenori Komatsu, Hiromi Kubota, Nobuyuki Tanaka, Hirotada Ohashi, Mariah Griffin, Jennifer Link, Glenn Geher, Maryanne L. Fisher
Summary: The study conducted an intervention experiment on information provision in different countries and found that the intervention was effective in all three countries, but the effect size varied. Women showed more significant intervention effects than men in Japan and the US, while no gender difference was observed in Canada. Higher agreeableness contributed significantly to the intervention effects. The COVID-19 pandemic weakened the intervention effect by increasing the message effect in the control group.
Article
Construction & Building Technology
Hidenori Komatsu, Osamu Kimura
Summary: Among the techniques supporting energy conservation, clustering has been widely used for customer segmentation. However, the potential of clustering techniques based on smart meter data analytics for small- and medium-sized enterprises to recognize building types has not been sufficiently investigated. This study evaluated two methods for clustering based on an open smart meter dataset and found that both methods were reasonable in estimating building types.
ENERGY AND BUILDINGS
(2023)
Article
Multidisciplinary Sciences
Hidenori Komatsu, Hiromi Kubota, Nobuyuki Tanaka
Summary: There is concern that information technology (IT) innovations may lead to job redundancy. Reminding people of familial support can help mitigate risk-averse attitudes towards risks that threaten future generations. A randomized controlled trial found that treatment groups receiving additional text or additional text with an illustration highlighting IT innovations showed a significant increase in the sense of familial support, compared to the control group. The study also explored how different components of personality traits influenced responses to the intervention messages.
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)