Scholarly record
APPLICATION OF THE ARTIFICIAL INTELLIGENCE IN THE ENERGY SOLVING OF BUILDING SERVICES OF INTELLIGENT BUILDINGS AREAS
Abstract
Currently, a great emphasis is put on the environment, energy intensity, and therefore on applications of the renewable energy sources (RES). Putting a large amount of RES into the modern distribution systems represents certain problems we can address, inter alia, the variable energy outputs of RES aggregated together, including their accumulation managed by respective energy management system (EMS). The result may be the optimization unit commitment of RES distributed in the micro-grids of a fictitious intelligent city composed of an intelligent buildings (IB) complex. For this purpose, a computer program has been implemented to optimize operating costs to cover energy consumption of intelligent buildings, based on predicted load profiles. The underlying basis of EMS is, among other things, the optimization unit commitment of RES distributed in the micro-grid, distant from a fictitious intelligent city composed of an intelligent buildings complex. For this purpose, a computer program has been implemented to optimize operating costs to cover energy consumption of intelligent buildings, based on predicted load profiles. As an example of the self-organizing neural network used for cluster analysis (CA), we demonstrate its efficiency in the process of identifying type daily energy consumption diagrams of an intelligent buildings complex combined in the electric micro-grid for a typical working day and typical day off based on networkпїЅs annual history. The aforementioned type daily diagrams can be used to predict the power consumption.
Publication Impact Profile
- Captures
- Mendeley - Readers: 5
Publication details
References0
Structured references will appear here after the reference import pass. The count is preserved now so the scholarly record is not incomplete.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.