| URI | http://purl.tuc.gr/dl/dias/1B444A3F-569E-48AB-9C37-A8C987EA5907 | - |
| Identifier | https://www.sciencedirect.com/science/article/pii/S1876610217347173?via%3Dihub | - |
| Identifier | https://doi.org/10.1016/j.egypro.2017.09.583 | - |
| Language | en | - |
| Extent | 10 pages | en |
| Title | Development and testing of a micro-grid excess power production forecasting algorithms | en |
| Creator | Mavrigiannaki Angeliki | en |
| Creator | Μαυριγιαννακη Αγγελικη | el |
| Creator | Kampelis Nikolaos | en |
| Creator | Καμπελης Νικολαος | el |
| Creator | Kolokotsa Dionysia | en |
| Creator | Κολοκοτσα Διονυσια | el |
| Creator | Marchegiani Daniele | en |
| Creator | Standardi, Laura | en |
| Creator | Isidori Daniela | en |
| Creator | Christalli Cristina | en |
| Publisher | Elsevier | en |
| Content Summary | Traditional electricity grids lack flexibility in power generation and load operation in contrast to smart-micro grids that form semi-autonomous entities with energy management capabilities. Load forecasting is invaluable to smart micro-grids towards assisting the implementation of energy management schedules for cost-efficient and secure operation. In the present paper is examined the 24h forecasting of excess production in an existing micro-grid. Alternative input parameters are considered for achieving an accurate prediction. The prediction can be used for scheduling the charging process of a thermal storage during weekends based on excess power production levels. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-06-06 | - |
| Date of Publication | 2017 | - |
| Subject | Artificial neural network | en |
| Subject | Forecasting | en |
| Subject | Integration | en |
| Subject | Micro-grid | en |
| Bibliographic Citation | A. Mavrigiannaki, N. Kampelis, D. Kolokotsa, D. Marchegiani, L. Standardi, D. Isidori and C. Christalli, "Development and testing of a micro-grid excess power production forecasting algorithms," in 9th International Conference on Sustainability and Energy in Buildings, 2017, pp. 654-663. doi: 10.1016/j.egypro.2017.09.583 | en |