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Optimal Residential House Energy Man...

Wang, Yanghao,

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Optimal Residential House Energy Management System with Thermal Prediction /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal Residential House Energy Management System with Thermal Prediction // Yanghao Wang.
作者:	Wang, Yanghao,
面頁冊數:	1 electronic resource (127 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Software. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28781430
ISBN:	9798494454676

Optimal Residential House Energy Management System with Thermal Prediction /
Wang, Yanghao,

Optimal Residential House Energy Management System with Thermal Prediction /Yanghao Wang. - 1 electronic resource (127 pages)

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

To achieve a higher energy efficiency and reduce CO2 emission during the process of house energy consumption based on the fast development of smart grid and smart home infrastructures. With the help of Inteb stainability and Centre for Global Eco-innovation (CGE), this project aims to develop a cost-effective energy management solution for residential houses. To accomplish that, an Intelligent web/app based IT solution for monitoring and controlling energy in buildings is developed.There are several outcomes from this entire solution. First of all, a building thermal model and the method to predict the temperature change for different types of buildings are proposed. This prediction takes very short time to formulate the trainer thus satisfy the plug and ready-to-use need of residential users'. This method is further used in our Web APP to help the automatic determination on house heating system operation. Then, a load scheduling optimisation algorithm has been developed to schedule the devices and appliances for domestic buildings, in order to minimise the energy cost for users while satisfying their comfort level. This algorithm takes day-ahead pricing from energy retailers and similar day energy consumption data into account. Users can predefine their preferred room temperature and appliances execution time. The exhaustive search method is used to solve the optimisation problem with optimal performance. The result shows higher energy efficiency than the previous method. Over 15% of energy consumption reduction is achieved with optimal load scheduling. A high environmental impact can be achieved if the load scheduling algorithm is widely used, resulting in a significant cut of carbon emissions due to reduction of energy consumption and more evenly distributed load across time. Finally, the principles we proposed are tested in a normal residential test house in Liverpool. For test reason and also further commercialisation, a user-friendly web based APP has been developed to become a potential commercial product of BE Thinking Ltd (previously branded Inteb Sustainability Ltd.). The main feature functions include: 1) machine learning based users' preference prediction and environmental parameter determination. 2) easy-to-use energy consumption monitoring and analysis. 3) Optimisation algorithm based automatic energy usage suggestion service.The effectiveness of our integrated and intelligent IT solution has been analysed by computer simulation and validated through a test house. The feedback from the users has shown the advances of our solution's special features over the existing products on the market, including considering users' preferences, machine learning and intelligent energy management advice etc.

English

ISBN: 9798494454676Subjects--Topical Terms:

619355
Software.

Optimal Residential House Energy Management System with Thermal Prediction /
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