Dingding Hu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Dingding Hu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
Fangyi Li
School of Management, Hefei University of Technology, Hefei, 230009, China
2 shared publicationsKaile Zhou
School of Management, Hefei University of Technology, Hefei, 230009, China
2 shared publicationsDawei Ma
1 shared publicationJiong Zhou
Jizhong Energy Technology Services (Shanghai) Company, Shanghai, China
1 shared publication- Kaile Zhou
School of Management, Hefei University of Technology, Hefei, China
1 shared publication - Rong Hu
School of Management, Hefei University of Technology, Hefei, China
1 shared publication
Latest Publications
- Open Access
- 6 December 2023
- Published by Springer Nature in Scientific Data
- Vol. 10 (1) , 1-10
High-resolution electric power load data of an industrial park with multiple types of buildings in China
Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.
- 6 December 2023
- Published by Springer Nature in Scientific Data
- Vol. 10 (1) , 1-10
Open AccessHigh-resolution electric power load data of an industrial park with multiple types of buildings in China
Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.
- 20 June 2022
- Published by Elsevier in Transport Policy
- Vol. 125, 164-178
Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data
- 20 June 2022
- Published by Elsevier in Transport Policy
- Vol. 125, 164-178
Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data