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Load Demand Forecasting: A case study for Greece
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, Energy Systems and Building Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

It is more than a fact that electrical energy is a main production factor of every economic activity. Since electrical power is not easy to store, it needs to be consumed as it is generated in order to keep a constant balance between supply and demand. As a result, for developing an efficient energy market it is significant to create a method for accurately forecasting the electricity consumption.

This thesis describes a method for analyzing data provided by the ENTSO-E transparency platform. The ENTSO-E (European Network of Transmission System Operators) is a network of electricity operators from 36 countries across Europe. Its main objective is to provide transparency concerning data of electricity generation and consumption in Europe in order to promote the development of efficient and competitive electricity markets.

By using the method described in this thesis, one may use historical data provided by ENTSO-E to forecast the electricity consumption of an EU country for the years to come. As an example, data of electricity consumption in Greece during the years 2015-2018 have been used in order to calculate the average load demand of a weekday during the year 2030.

On the other hand, in order to correctly predict the electricity demand of a specific region over the next decade, one should take into account some crucial parameters that may influence not only the evolution of the load demand, but also the fuel mix that will be used in order to cover our future electricity needs. Advances in power generation technologies, evolution of fuel prices, expansion of electricity grid and economic growth are a subset of parameters that should be taken into account for an accurate forecast of the electricity consumption in the long run.

Particularly for Greece, a set of parameters that may affect the electricity consumption are being computationally analyzed in order to evaluate their contribution to the load demand curve by the year 2030. These include the interconnection of Greek islands to the mainland, the development of Hellinikon Project and the increase of the share of electric vehicles.

The author of this thesis has developed code in Python programming language that can be found in the Appendix. These scripts and functions that implement most of the calculations described in the following chapters can also be used for forecasting the load demand of other EU countries that are included in the ENTSO-E catalogue. The datasets used as input to these algorithms may also be used from the readers to identify more patterns for predicting the load demand for a specific region and time.

A sustainable energy system is based on consumers with environmental awareness. As a result, citizens living inside the European Union should become a member of a community that promotes energy saving measures, investments in renewable energy sources and smart metering applications.

Place, publisher, year, edition, pages
2019. , p. 60
Keywords [en]
ENTSO-E, Load Demand Curve, Spatial Load Forecasting, Python, Demand Side Management, Hellinikon Project, Non Interconnected Islands, Electric Vehicles, Renewable Energy Sources.
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:hig:diva-29841OAI: oai:DiVA.org:hig-29841DiVA, id: diva2:1323972
Subject / course
Energy systems
Educational program
Energy systems – master’s programme (two years)
Supervisors
Examiners
Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-06-19Bibliographically approved

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CiteExportLink to record
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Citation style
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