Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review
W Strielkowski, A Vlasov, K Selivanov, K Muraviev… - Energies, 2023 - mdpi.com
The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …
systems offers the potential to accurately predict and manage the behavior of these systems …
A comprehensive review on deep learning approaches for short-term load forecasting
The balance between supplied and demanded power is a crucial issue in the economic
dispatching of electricity energy. With the emergence of renewable sources and data-driven …
dispatching of electricity energy. With the emergence of renewable sources and data-driven …
Load forecasting with machine learning and deep learning methods
Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …
buildings without any structural change and is the basis for controlling and optimizing …
Review of the data-driven methods for electricity fraud detection in smart metering systems
In smart grids, homes are equipped with smart meters (SMs) to monitor electricity
consumption and report fine-grained readings to electric utility companies for billing and …
consumption and report fine-grained readings to electric utility companies for billing and …
[HTML][HTML] A hybrid stacking model for enhanced short-term load forecasting
The high penetration of distributed energy resources poses significant challenges to the
dispatch and operation of power systems. Improving the accuracy of short-term load …
dispatch and operation of power systems. Improving the accuracy of short-term load …
A survey on key management and authentication approaches in smart metering systems
The implementation of the smart grid (SG) and cyber-physical systems (CPS) greatly
enhances the safety, reliability, and efficiency of energy production and distribution. Smart …
enhances the safety, reliability, and efficiency of energy production and distribution. Smart …
Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review
Access to electricity is a cornerstone for sustainable development and is pivotal to a
country's progress. The absence of electricity impedes development and elevates poverty …
country's progress. The absence of electricity impedes development and elevates poverty …
Short-term load forecasting in smart grids using hybrid deep learning
Load forecasting in Smart Grids (SG) is a major module of current energy management
systems, that play a vital role in optimizing resource allocation, improving grid stability, and …
systems, that play a vital role in optimizing resource allocation, improving grid stability, and …
Smart grids in industrial paradigms: a review of progress, benefits, and maintenance implications: analyzing the role of smart grids in predictive maintenance and the …
This study provides a comprehensive analysis of the integration and impact of smart grids in
the industrial sector, focusing on the evolution from traditional grids, the role of predictive …
the industrial sector, focusing on the evolution from traditional grids, the role of predictive …
A traffic analysis and node categorizationaware machine learning-integrated framework for cybersecurity intrusion detection and prevention of WSNs in smart grids
Smart grids are transforming the generation, distribution, and consumption of power,
marking a revolutionary step forward for contemporary energy systems. Communication in …
marking a revolutionary step forward for contemporary energy systems. Communication in …