LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: Elsevier
Languages: English
Types: Article
Subjects: TA
Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy\ud generation potential, electricity tariffs, and occupants’ behaviour (i.e. interactions with their\ud appliances). The paper presents an ANN–GA (Artificial Neural Network / Genetic Algorithm) smart\ud appliance scheduling approach for optimized energy management in the domestic sector. The\ud proposed approach reduces energy demand in “peak” periods, maximizes use of renewable sources\ud (PV and wind turbine), while reducing reliance on grid energy. Comprehensive parameter\ud optimization has been carried out for both ANN and GA to find the best combinations, resulting in\ud optimum weekly schedules. The proposed artificial intelligence techniques involve a holistic\ud understanding of (near) real-time energy demand and supply within a domestic context to deliver\ud optimized energy usage with minimum computational needs. The solution is stress-tested and\ud demonstrated in a four bedroom house with grid energy usage reduction by 10%, 25%, and 40%,\ud respectively.

Share - Bookmark

Cite this article