Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods
| dc.date.accessioned | 2021-03-10T14:03:34Z | |
| dc.date.available | 2021-03-10T14:03:34Z | |
| dc.date.issued | 2017-04-05 | |
| dc.identifier | doi:10.17170/kobra-202103103474 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/12606 | |
| dc.description | This is a revised manuscript of the article "Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods". The final authenticated version is available online at: https://doi.org/10.1016/j.energy.2017.03.166 | ger |
| dc.language.iso | eng | eng |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | renewable energy systems | eng |
| dc.subject | Monte Carlo methods | eng |
| dc.subject | hotels | eng |
| dc.subject | Canary Island | eng |
| dc.subject.ddc | 333 | |
| dc.title | Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods | eng |
| dc.type | Aufsatz | |
| dcterms.abstract | This paper investigates the use of renewable energies to supply hotels in island regions. The aim is to evaluate the effect of weather and occupancy fluctuations on the sensitivity of investment criteria. The sensitivity of the chosen energy system is examined using a Monte Carlo simulation considering stochastic weather data, occupancy rates and energy needs. For this purpose, algorithms based on measured data are developed and applied to a case study on the Canary Islands. The results underline that electricity use in hotels is by far the largest contributor to their overall energy cost. For the invested hotel on the Canary Islands, the optimal share of renewable electricity generation is found to be 63%, split into 67% photovoltaic and 33% wind power. Furthermore, a battery is used to balance the differences between day and night. It is found, that the results are sensitive to weather fluctuations as well as economic parameters to about the same degree. The results underline the risk caused by using reference time series for designing energy systems. The Monte Carlo method helps to define the mean of the annuity more precisely and to rate the risk of fluctuating weather and occupancy better. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Meschede, Henning | |
| dcterms.creator | Dunkelberg, Heiko | |
| dcterms.creator | Stöhr, Fabian | |
| dcterms.creator | Peesel, Ron-Hendrik | |
| dcterms.creator | Hesselbach, Jens | |
| dc.relation.doi | doi:10.1016/j.energy.2017.03.166 | |
| dc.subject.swd | Kanarische Inseln | ger |
| dc.subject.swd | Erneuerbare Energien | ger |
| dc.subject.swd | Monte-Carlo-Simulation | ger |
| dc.subject.swd | Hotel | ger |
| dc.subject.swd | Energieverbrauch | ger |
| dc.type.version | acceptedVersion | |
| dcterms.source.identifier | EISSN 0360-5442 | |
| dcterms.source.journal | Energy | eng |
| dcterms.source.pageinfo | 86-100 | |
| dcterms.source.volume | Volume 128 | |
| kup.iskup | false |
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