High particulate matter (PM) concentrations in the cleanroom semiconductor factory have become a significant concern as they can damage electronic devices during the manufacturing process. PM can be predicted before becoming more concentrated based o...
Short-term load forecasting (STLF) is essential for urban sustainable development. It can further contribute to the stable operation of the smart grid. With the development of renewable energy, improving STLF accuracy has become a vital task. Neverth...
The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of...
Extreme weather events present some of the most severe natural threats to the electric grid, and accurate load forecasting during those events is essential for grid management and disaster preparedness. In this study, we evaluate the effectiveness of...
Short-term load forecasting (STLF), especially for regional aggregate load forecasting, is essential in smart grid operation and control. However, the existing CNN-based methods cannot efficiently extract the essential features from the electricity l...
As global climate change escalates, wildfires have emerged as a critical form of natural disaster, presenting substantial risks to ecosystems, public safety, and economic development. While satellite remote sensing has been extensively utilized for w...
The emerging scope of the Industrial Internet of Services (IIoS) requires a robust intrusion detection system to detect malicious attacks. The increasing frequency of sophisticated and high-impact cyber attacks has resulted in financial losses and ca...