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Dawei Pan
Researcher at Harbin Engineering University
Publications - 11
Citations - 292
Dawei Pan is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Energy consumption & Energy conservation. The author has an hindex of 6, co-authored 10 publications receiving 212 citations. Previous affiliations of Dawei Pan include Harbin Institute of Technology & Hong Kong Polytechnic University.
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Journal ArticleDOI
Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning
TL;DR: The proposed on-line training strategy achieves a better prediction precision as well as improves the operating efficiency for battery RUL estimation and presents an incremental optimized RVM algorithm to the model via efficient on-lines training.
Proceedings ArticleDOI
Thermal Inertia: Towards an energy conservation room management system
TL;DR: A green room management system with three main components that has a wireless sensor network to collect indoor, outdoor temperature and electricity expenses of the air-conditioning devices and an energy-temperature correlation model for the energy expenses and the corresponding room temperature.
Proceedings ArticleDOI
Minimizing building electricity costs in a dynamic power market: algorithms and impact on energy conservation
TL;DR: This paper investigates how to cut the electricity bills of commercial buildings in a dynamic power market by developing a holistic planning of electricity purchasing schedule with thermal storage management, and appropriate room assignment schedules for classes/meetings usage.
Journal ArticleDOI
A study towards applying thermal inertia for energy conservation in rooms
TL;DR: This article makes a key observation that after a meeting or a class ends in a room, the indoor temperature will not immediately increase to the outdoor temperature, and proposes a framework for exploring thermal inertia in room management.
Journal ArticleDOI
Transfer Learning-Based Hybrid Remaining Useful Life Prediction for Lithium-Ion Batteries Under Different Stresses
TL;DR: The experimental results show that the method is effective for the RUL prediction problem of the mapping under different working conditions, and the relative error is less than 5% when the data length is 50%, which proves the application potential of the method.