Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …
potential to change our energy supply, trade, and consumption dramatically. The new …
[HTML][HTML] Distributed energy systems: A review of classification, technologies, applications, and policies
The sustainable energy transition taking place in the 21st century requires a major
revamping of the energy sector. Improvements are required not only in terms of the …
revamping of the energy sector. Improvements are required not only in terms of the …
Overview of smart grid implementation: Frameworks, impact, performance and challenges
High consumption and ever-increasing demand for electricity at commercial, residential, and
industrial levels have attracted the research community to look for new technologies for the …
industrial levels have attracted the research community to look for new technologies for the …
A survey on the detection algorithms for false data injection attacks in smart grids
Cyber-physical attacks are the main substantial threats facing the utilization and
development of the various smart grid technologies. Among these attacks, false data …
development of the various smart grid technologies. Among these attacks, false data …
Review of smart meter data analytics: Applications, methodologies, and challenges
The widespread popularity of smart meters enables an immense amount of fine-grained
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
[HTML][HTML] Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
S Barja-Martinez, M Aragüés-Peñalba… - … and Sustainable Energy …, 2021 - Elsevier
Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …
systems by extracting value from the data generated by the deployed metering and sensing …
Electricity theft detection in smart grid systems: A CNN-LSTM based approach
Among an electricity provider's non-technical losses, electricity theft has the most severe and
dangerous effects. Fraudulent electricity consumption decreases the supply quality …
dangerous effects. Fraudulent electricity consumption decreases the supply quality …
Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids
Electricity theft is harmful to power grids. Integrating information flows with energy flows,
smart grids can help to solve the problem of electricity theft owning to the availability of …
smart grids can help to solve the problem of electricity theft owning to the availability of …
[HTML][HTML] Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment
Smart meters are key elements of a smart grid. These data from Smart Meters can help us
analyze energy consumption behaviour. The machine learning and deep learning …
analyze energy consumption behaviour. The machine learning and deep learning …
Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing
For the smart grid energy theft identification, this letter introduces a gradient boosting theft
detector (GBTD) based on the three latest gradient boosting classifiers (GBCs): 1) extreme …
detector (GBTD) based on the three latest gradient boosting classifiers (GBCs): 1) extreme …