Forecasting Electricity Infeed for Distribution System Networks: An Analysis of the Dutch Case

Energy, Forthcoming

Posted: 18 May 2013 Last revised: 19 Jul 2013

See all articles by Fehmi Tanrisever

Fehmi Tanrisever

Bilkent University, Faculty of Business Administration

Kursad Derinkuyu

University of Turkish Aeronautical Association

Michael Heeren

Capgemini Consulting

Date Written: May 17, 2013

Abstract

Estimating and managing electricity distribution losses are the core business competencies of distribution system operators (DSOs). Since electricity demand is a major driver of network losses, it is essential for DSOs to have an accurate estimate of the electricity infeed in their network. In this paper, motivated by the operations of a Dutch electricity distribution system operator, we examine how to estimate the electricity infeed in distribution networks one year in advance with hourly forecasting intervals, so that the DSOs may effectively hedge for their physical losses in the wholesale markets.

We identify the relevant factors for DSOs to forecast the electricity infeed in their networks, and to quantify their effects. We show that most of the calendar variables, such as national holidays, bridge days as well as days near holidays have a significant effect on electricity infeed. Our analysis reveals that the impact of calendar variables significantly depends on the hour of the day. On the other hand, economic and demographic factors do not seem to influence the electricity infeed for the planning horizon of DSOs. We also explore the influence of meteorological factors on the electricity infeed in the Netherlands. Finally, we develop and compare methods for electricity infeed forecasting, based on multiple regression and time series analysis. Our analysis reveals that the regression-based method outperforms the time series-based method on the average measures whereas the time series-based method is better in the worst case analysis. Hence, we point out that the forecasting methods used by DSOs may have significant implications on their financial hedging policies.

Keywords: Electricity demand forecasting, electricity distribution, multiple regression, time series

JEL Classification: C22, C32, Q40

Suggested Citation

Tanrisever, Fehmi and Derinkuyu, Kursad and Heeren, Michael, Forecasting Electricity Infeed for Distribution System Networks: An Analysis of the Dutch Case (May 17, 2013). Energy, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2266257

Fehmi Tanrisever (Contact Author)

Bilkent University, Faculty of Business Administration ( email )

Bilkent
Ankara, 06800
Turkey

Kursad Derinkuyu

University of Turkish Aeronautical Association ( email )

Bahçekapı Quarter Okul Street No:11
Ankara, 06790
Turkey

Michael Heeren

Capgemini Consulting ( email )

Gustavslundsvägen 131
P.O. Box 825
Bromma, 161 24
Sweden

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