Projects per year
Abstract
The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This darkgrey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills.
Original language | English |
---|---|
Pages (from-to) | 3550-3560 |
Number of pages | 11 |
Journal | IEEE Transactions on Smart Grid |
Volume | 14 |
Issue number | 5 |
Early online date | 10 Jan 2023 |
DOIs | |
Publication status | Published - 1 Sept 2023 |
Funding
This work was supported in part by the British Council through the Project ZELEC under Grant 515761951.
Keywords
- Atmospheric modeling
- Computational modeling
- Data models
- Heating systems
- Representation learning
- Temperature measurement
- Thermal model
- Zigbee
- data dependency
- edge computing
- machine learning
ASJC Scopus subject areas
- General Computer Science
Fingerprint
Dive into the research topics of 'An IoT-based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy Management'. Together they form a unique fingerprint.Projects
- 1 Finished
-
High Energy and Power Density (HEAPD) Solutions to Large Energy Deficits
Li, F. (PI), Redfern, M. (CoI) & Walker, I. (CoI)
Engineering and Physical Sciences Research Council
30/06/14 → 29/12/17
Project: Research council