Abstract
This paper presents a new methodology for the estimation of power capacity profiles for smart buildings. The capacity profile can be used within a demand-side management system in order to guide the building temperature operation. It provides a trade-off between the quality of service perceived by the end user and the requirements from the grid in a demand-response context. We use a data-fitting approach and a multiclass classifier to compute the required profile to run a set of electric heating and cooling units via an admission control module. Simulation results validate the performance of the proposed methodology under various conditions, and we compare our approach with neural networks in a real-world-based scenario.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
