Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions

A Kumari, R Gupta, S Tanwar, N Kumar - Journal of Parallel and Distributed …, 2020 - Elsevier
In the recent years, the Smart Grid (SG) system faces various challenges like the ever-
increasing energy demand, the enormous growth of renewable energy sources (RES) with …

A review on intelligent energy management systems for future electric vehicle transportation

Z Teimoori, A Yassine - Sustainability, 2022 - mdpi.com
Over the last few years, Electric Vehicles (EVs) have been gaining interest as a result of their
ability to reduce vehicle emissions. Developing an intelligent system to manage EVs …

Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland

Z Guo, JR O'Hanley, S Gibson - Utilities Policy, 2022 - Elsevier
We use machine learning algorithms to investigate various aspects of residential electricity
consumption for households in the Republic of Ireland. Temperature, day of week, and …

Influence of geodemographic factors on electricity consumption and forecasting models

JP Singh, O Alam, A Yassine - IEEE Access, 2022 - ieeexplore.ieee.org
The residential sector is a major consumer of electricity, and its demand will rise by 65
percent by the end of 2050. The electricity consumption of a household is determined by …

[HTML][HTML] Transformations of trust in society: A systematic review of how access to big data in energy systems challenges Scandinavian culture

J de Godoy, K Otrel-Cass, KH Toft - Energy and AI, 2021 - Elsevier
In the era of information technology and big data, the extraction, commodification, and
control of personal information is redefining how people relate and interact. However, the …

[HTML][HTML] Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

J Wu, Z Niu, X Li, L Huang, PS Nielsen, X Liu - Energy, 2023 - Elsevier
Understanding energy consumption patterns is crucial for energy demand-side
management. Unlike traditional data mining or machine learning-based methods, this paper …

A federated learning model with short sequence to point mechanism for smart home energy disaggregation

S Kaspour, A Yassine - 2022 IEEE Symposium on Computers …, 2022 - ieeexplore.ieee.org
Residential households contribute significantly to the overall energy consumption in
developed countries. To reduce their energy consumption, they need solutions that help …

An incremental clustering algorithm with pattern drift detection for IoT-enabled smart grid system

Z Jiang, R Lin, F Yang - Sensors, 2021 - mdpi.com
The IoT-enabled smart grid system provides smart meter data for electricity consumers to
record their energy consumption behaviors, the typical features of which can be represented …

Machine learning based short-term load forecasting for smart meter energy consumption data in london households

DA Bashawyah, SM Qaisar - 2021 IEEE 12th International …, 2021 - ieeexplore.ieee.org
The eolved deployment of smart meters has enabledan extensive authority and monitoring
on both electricity operating companies and customers. Based on smart meters, it is possible …

Parametric Time-series Modelling of London Smart Meter Data for Short-term Demand Forecasting

A Mondal, S Das - 2023 IEEE 3rd International Conference on …, 2023 - ieeexplore.ieee.org
Electricity being one of the most important components behind economic growth in 21st
century, accurate electricity demand forecast became essential. Now with the deployment of …