With the growth in energy consumption, demand response (DR) programs in the power
network have gained popularity and can be expected to become more widespread in the …

Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise
in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation …

An increase in emission price creates economic incentives because bioenergy producers
could sell bioenergy and realize values attached to emission offset. Recently, the …

Under the umbrella of a smart grid environment, demand response (DR) is a comprehensive
way to make the best use of household energy consumption. DR refers to the rescheduling …

Purpose For a public servant (PS) to be innovative, he or she needs to gather and process
enough vital information from budget setting processes. However, research addressing how …

This paper addresses the innovation management problem of financial trading strategies for
power system planning through the utilization of the BO-BERT-GRNN model. The BO-BERT …

Abstract The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure
(AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and …

Demand response, and artificial intelligence integration with it, have a considerable effect in
optimizing energy consumption, grid stability, and promoting sustainable energy practices …

Mobile edge computing (MEC) has emerged as a pivotal technology to address the
computational demands of resource-constrained mobile devices by offloading tasks to …

This study focuses on the analysis and improvement of clustering algorithms for smart grid
big data analysis. The goal is to address the unique challenges posed by smart grid data …