The share of residential building energy consumption in global energy consumption has
rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption …

Nowadays, systems or entities are usually monitored by devices, generating large amounts
of time series. Detecting anomalies in them help prevent potential losses, thus arousing …

Improving the detection rate of electricity theft users in smart grids is crucial to the safe
operation of the power system and the economic efficiency of the grid. The traditional …

In the era of the Internet of Things (IoT), where smartphones, built-in systems, wireless
sensors, and nearly every smart device connect through local networks or the internet …

Offshore wind turbines (OWTs) play a crucial role in renewable energy generation, but their
remote and harsh environments make them prone to various anomalies that can significantly …

Anomalies in switches behavior in railway systems can significantly impact operational
efficiency and safety. This paper proposes an approach based on energy consumption data …

In this paper, The traditional Recurrent Neural Network (RNN) can predict the time series
data. However, there is always a certain deviation between the predicted value and the real …

Rising impacts of human-induced climate change in developing countries have spurred
government policies, activism, and sustainability research aimed at curbing energy …

L'énergie représente les deux tiers des émissions totales de gaz à effet de serre, de sorte
que le secteur de l'énergie est l'acteur central dans les efforts visant à réduire les émissions …

The individual rapidly navigating the digital technology landscape has been instrumental in
transforming industrial processes due to the deep integration between physical and digital …