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Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/827B1A86-0898-4EBB-B597-B917B2270379 | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.76575 | - |
| Language | en | - |
| Extent | 5.162 kilobytes | en |
| Title | Development of DR energy management optimization at building and district level using GA and NN modeling power predictions | en |
| Creator | Tsekeri Elisavet | en |
| Creator | Τσεκερη Ελισαβετ | el |
| Contributor [Thesis Supervisor] | Kolokotsa Dionysia | en |
| Contributor [Thesis Supervisor] | Κολοκοτσα Διονυσια | el |
| Contributor [Committee Member] | Kalaitzakis Konstantinos | en |
| Contributor [Committee Member] | Καλαϊτζακης Κωνσταντινος | el |
| Contributor [Committee Member] | Karatzas Giorgos | en |
| Contributor [Committee Member] | Καρατζας Γιωργος | el |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Environmental Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Μηχανικών Περιβάλλοντος | el |
| Content Summary | In broad terms, Demand Response refers to the operational, regulatory and technical framework for inducing changes in the power demand of buildings or settlements during the day. Time of Use (ToU) pricing can be vital to leverage advancements in building or district energy management systems to shift loads, exploit storage capabilities, increase renewable energy penetration and ultimately relief stress from the grid. This is an important feature of the smart grid and a step closer to the necessary open and transparent market framework according to which energy consumption costs reflect actual costs of production, transmission, distribution, infrastructure maintenance and upgrade etc. In this paper Neural Network power predictions are performed and a genetic algorithm based framework for energy management in a group of buildings is developed and tested on real data. According to the results ToU pricing could be exploited by the industry using ANN based day ahead prediction to perform load shifting and minimize associated costs. | en |
| Type of Item | Μεταπτυχιακή Διατριβή | el |
| Type of Item | Master Thesis | en |
| License | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| Date of Item | 2018-06-20 | - |
| Date of Publication | 2018 | - |
| Subject | Genetic algorithms | en |
| Subject | Artificial neural network | en |
| Subject | Demand response | en |
| Bibliographic Citation | Elisavet Tsekeri, "Development of DR energy management optimization at building and district level using GA and NN modeling power predictions", Master Thesis, School of Environmental Engineering, Technical University of Crete, Chania, Greece, 2018 | en |
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