Dynamic variation in dew-point temperature of attached air layer of radiant ceiling cooling panels

• 作者: Chen Bi Choi Bong Soo Jingda Ma Pan Gao Wufeng Jin Zhiqiang Wang
• 来源期刊: 建筑模拟（英文版）
• 年，卷（期）: 2020,（6）
• 所属期刊栏目: BUILDING SYSTEMS AND COMPONENTS
• 页码范围: 1281-1290
• 页数: 10页
• 语种: 英文

摘要

The present article describes the integration of a data-driven predictive demand response control for residential buildings with heat pump and on-site energy generation. The data driven control approach schedules the heating system of the building. In each day, the next 24 hours heating demand of buildings, including space heating and domestic hot water consumption, are predicted by means of a hybrid wavelet transformation and a dynamic neural network. Linear programming is implemented to define a cost-optimal schedule for the heat pump operation. Moreover, the study discusses the impact of heat demand prediction error on performance of demand response control. In addition, the option of energy trading with the electrical grid is considered in order to evaluate the possibility of increasing the profit for private householders through on-site energy generation. The results highlight that the application of the proposed predictive control could reduce the heating energy cost up to 12％ in the cold Finnish climate. Furthermore, on-site energy generation declines the total energy cost and consumption about 43％ and 24％ respectively. The application of a data-driven control for the demand prediction brings efficiency to demand response control.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Wufeng Jin		1	0	0.0	0.0
2	Jingda Ma		1	0	0.0	0.0
3	Chen Bi		1	0	0.0	0.0
4	Zhiqiang Wang		1	0	0.0	0.0
5	Choi Bong Soo		1	0	0.0	0.0
6	Pan Gao		1	0	0.0	0.0