From cloud down to things: An overview of machine learning in internet of things
With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to
meet the requirement of all IoT applications. The limited computation and communication …
meet the requirement of all IoT applications. The limited computation and communication …
AI-empowered methods for smart energy consumption: A review of load forecasting, anomaly detection and demand response
This comprehensive review paper aims to provide an in-depth analysis of the most recent
developments in the applications of artificial intelligence (AI) techniques, with an emphasis …
developments in the applications of artificial intelligence (AI) techniques, with an emphasis …
Deep learning-based short-term load forecasting approach in smart grid with clustering and consumption pattern recognition
Different aggregation levels of the electric grid's big data can be helpful to develop highly
accurate deep learning models for Short-term Load Forecasting (STLF) in electrical …
accurate deep learning models for Short-term Load Forecasting (STLF) in electrical …
A review for green energy machine learning and AI services
Y Mehta, R Xu, B Lim, J Wu, J Gao - Energies, 2023 - mdpi.com
There is a growing demand for Green AI (Artificial Intelligence) technologies in the market
and society, as it emerges as a promising technology. Green AI technologies are used to …
and society, as it emerges as a promising technology. Green AI technologies are used to …
A machine learning model ensemble for mixed power load forecasting across multiple time horizons
The increasing penetration of renewable energy sources tends to redirect the power
systems community's interest from the traditional power grid model towards the smart grid …
systems community's interest from the traditional power grid model towards the smart grid …
Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks
N Giamarelos, EN Zois - Sustainable Energy, Grids and Networks, 2024 - Elsevier
Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …
demand for renewable energy sources, extensive urbanization, climate and energy crisis …
Daily plant load analysis of a hydropower plant using machine learning
The energy demand is increasing day by day, and to provide reliable power to everyone is a
challenging work. Power requirement is varying in nature, and it is dependent on the type of …
challenging work. Power requirement is varying in nature, and it is dependent on the type of …
Forecasting of energy consumption and production using recurrent neural networks
Energy forecasting for both consumption and production is a challenging task as it involves
many variable factors. It is necessary to calculate the actual production of energy and its …
many variable factors. It is necessary to calculate the actual production of energy and its …
Online hour-ahead load forecasting using appropriate time-delay neural network based on multiple correlation–multicollinearity analysis in IoT energy network
To meet up fluctuations of the real-time electric load demands, many electricity markets have
gone for the real-time-market-based operation. To do so, online forecasting of the real-time …
gone for the real-time-market-based operation. To do so, online forecasting of the real-time …
Short term load forecasting using bootstrap aggregating based ensemble artificial neural network
Background: Short Term Load Forecasting (STLF) can predict load from several minutes to
week plays a vital role to address challenges such as optimal generation, economic …
week plays a vital role to address challenges such as optimal generation, economic …