Szczegóły publikacji
Opis bibliograficzny
Prediction of cooling energy consumption in hotel building using machine learning techniques / Marek BOROWSKI, Klaudia ZWOLIŃSKA // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073. — 2020 — vol. 13 iss. 23 spec. iss.: Thermal behaviour, energy efficiency in buildings and sustainable construction, art. no. 6226, s. 1–19. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 17–19, Abstr. — Publikacja dostępna online od: 2020-11-26
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 131383 |
|---|---|
| Data dodania do BaDAP | 2020-12-30 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en13236226 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
The diversification of energy sources in buildings and the interdependence as well as communication between HVAC installations in the building have resulted in the growing interest in energy load prediction systems that enable proper management of energy resources. In addition, energy storage and the creation of energy buffers are also important in terms of proper resource management, for which it is necessary to correctly determine energy consumption over time. It is obvious that the consumption of cooling energy depends on meteorological conditions. Knowing the parameters of the outside air and the number of users, it is, therefore, possible to determine the hourly energy consumption of a cooling system in a building with some accuracy. The article presents models of cooling energy prediction in summer for a hotel building in southern Poland. The paper presents two methods that are often used for energy prediction: neural networks and support vector machines. Meteorological data, time data, and occupancy level were used as input parameters. Based on the collected input and output data, various configurations were tested to identify the model with the best accuracy. As the analysis showed, higher prediction accuracy was obtained thanks to the use of neural networks. The best of the proposed models was characterized by the WAPE and CV coefficients of 19.93% and 27.03%, respectively.