Description

Load shifting entails moving home electricity use from on-peak hours to off-peak hours, thus resulting in reduced utility bills. Given the Time of Use (TOU)rate, the load shifting can be achieved by scheduling the smart home appliances in advance. However, the operations of the home appliances interfere witheach other. For example, the heat dissipation of some appliances might contribute to the cooling load for the HVAC system. In contrast, a lower indoorair temperature might affect the heat loss rate of other appliances. In this study, we developed a connected home energy management system including differenttypes of appliances.2R2C thermal model was used to predict the behavior of the HVAC system, whose operation is controlled thermostatically. Similarly, a first-orderdifferential equation was used for the water heater system. To achieve more effective control, we adopted a learning-based system identification frameworkfor the 2R2C model and characterized three major coupling effects between different appliances. Peak load management is also considered in the model toavoid possible financial penalties caused by the high demand in a short amount of time.A case study was carried out by scheduling the operations of the appliances 24 hours ahead in a single-story test house located in Norman, Oklahoma. Theresults with data obtained from a smart thermostat indicated that the 2R2C model was accurate with a mean absolute error as low as 0.559 F. The 24-hour scheduling results indicated that most appliances tried to avoid the on-peak hours, the HVAC system chose to precool the air, and the water heaterchose to preheat the water. The proposed energy management system can be easily adapted into the IoT-based home automation system.

Product Details

Published:

2022

Number of Pages:

9

Units of Measure:

Dual

File Size:

1 file , 2.3 MB

Product Code(s):

D-TO-22-C075

Note:

This product is unavailable in Russia, Belarus