Wholesale electricity market analysis and price forecasting
| dc.contributor.advisor | Polemis, Michael | |
| dc.contributor.advisor | Πολέμης, Μιχαήλ | |
| dc.contributor.author | Zografos, Christos | |
| dc.date.accessioned | 2021-07-05T06:59:24Z | |
| dc.date.available | 2021-07-05T06:59:24Z | |
| dc.date.issued | 2021-05-18 | |
| dc.identifier.uri | https://dione.lib.unipi.gr/xmlui/handle/unipi/13530 | |
| dc.identifier.uri | http://dx.doi.org/10.26267/unipi_dione/953 | |
| dc.format.extent | 109 | el |
| dc.language.iso | en | el |
| dc.publisher | Πανεπιστήμιο Πειραιώς | el |
| dc.rights | Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ | * |
| dc.title | Wholesale electricity market analysis and price forecasting | el |
| dc.type | Master Thesis | el |
| dc.contributor.department | Σχολή Οικονομικών, Επιχειρηματικών και Διεθνών Σπουδών. Τμήμα Διεθνών και Ευρωπαϊκών Σπουδών | el |
| dc.description.abstractEN | This thesis aims to implement a forecasting technique, to predict the 24 market-clearing prices of the day-ahead electric energy market of Hungary, Italy, Greece, and Bulgaria. The analysis is based on a dynamic regression model implemented in Conejo et al (2005) for the Italian wholesale market. This thesis attempts to conduct a similar approach, to evaluate if a dynamic regression model, using the Ordinary Least Squared and the Fully-Modified Ordinary Least Squared methodology, is optimal for the implementation in different countries, with different energy characteristics. To conduct the forecast analysis, the forecasted values of demand and renewable energy production are used from the European Network of Transmission System Operators for electricity website. The relevant approach leads to significant results for Hungary, Italy, and Greece, while for Bulgaria the results for the sample period are not presented, since the deviation from the actual prices does not lead to a good forecast approach. The sample period is 2019, including one week for every season of the year. The proposed technique can be used or to be upgraded by electric energy market participants, since this analysis is based on only two variables, by using additional variables in the analysis, which affect the final price, or by using better-forecasted values. | el |
| dc.contributor.master | Ενέργεια: Στρατηγική, Δίκαιο & Οικονομία | el |
| dc.subject.keyword | Energy | el |
| dc.subject.keyword | Electricity | el |
| dc.subject.keyword | Forecasting | el |
| dc.date.defense | 2021-05-21 |
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Τμήμα Διεθνών και Ευρωπαϊκών Σπουδών
Department Of International And European Studies
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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα