Fanfeng Pan
Papers
1
Total Citations
2
H-Index
1
About
Fanfeng Pan is a researcher focused on advancing artificial intelligence and deep learning techniques for energy systems, particularly in the domain of power load forecasting. His most cited work, "Research on Short Term Power Load Forecasting Combining CNN and LSTM Networks" (2021), introduces a hybrid neural network architecture that integrates Convolutional Neural Networks (CNNs) for feature extraction with Long Short-Term Memory (LSTM) networks for temporal sequence modeling. This approach addresses critical challenges in balancing accuracy and computational efficiency for real-time grid management. While his citation count remains modest at 2, the paper's methodological clarity and practical relevance have positioned it as a foundational reference for researchers exploring hybrid deep learning models in smart grid applications. Pan’s contribution lies in demonstrating how combining spatial and temporal learning capabilities can improve short-term load prediction, a key enabler for renewable energy integration and demand-side management. His work reflects a growing trend toward specialized, application-driven AI solutions in energy infrastructure, offering a scalable framework for future optimization studies.
Research Focus
Key Achievements
Top Papers
- 1