Yineng Zhuang
Papers
1
Total Citations
2
H-Index
1
About
Yineng Zhuang is a researcher focused on advancing intelligent methods for energy systems, with a particular emphasis on short-term power load forecasting. Their most cited work, "Research on Short Term Power Load Forecasting Combining CNN and LSTM Networks" (2021), introduces a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. This approach effectively captures both spatial and temporal dependencies in load data, offering improved accuracy over traditional forecasting techniques—a critical contribution for grid stability and energy management. While their citation count is still growing, this foundational paper has garnered early recognition for its practical relevance in smart grid and renewable energy integration contexts. Zhuang’s work sits at the intersection of artificial intelligence and electrical engineering, demonstrating how neural architectures can be tailored to solve real-world operational challenges. As the demand for precise load forecasting increases with the rise of distributed energy resources, Zhuang’s research provides a scalable, data-driven framework that holds promise for both academic inquiry and industry application. Their contributions are particularly valuable for students and practitioners exploring deep learning applications in energy systems.
Research Focus
Key Achievements
Top Papers
- 1