Volatility in Electrical Load Forecasting for Long-term Horizon

An ARIMA-GARCH Approach

Conference paper (2016)

Authors

S.R. Khuntia Intelligent Electrical Power Grids -

José L. Rueda Intelligent Electrical Power Grids -

M.A.M.M. van der Meijden TenneT TSO B.V., Intelligent Electrical Power Grids -

Research Group

Intelligent Electrical Power Grids () (TU Delft)

DOI

https://doi.org/10.1109/PMAPS.2016.7764184

ARIMA GARCH Volatility ARCH Long-term load forecast

More Info

expand_more

To reference this document use:

http://resolver.tudelft.nl/uuid:21dd52e4-5862-459e-8a08-4ef25489da76

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

Abstract

Electrical load forecasting in long-term horizon of power systems plays an important role for system planning and development. Load forecast in long-term horizon is represented as time-series. Thus, it is important to check the effect of volatility in the forecasted load time-series. In short, volatility in long-term horizon affects four main actions: risk management, long-term actions, reliability, and bets on future volatility. To check the effect of volatility in load series, this paper presents a univariate time series-based load forecasting technique for long-term horizon based on data corresponding to a U.S. independent system operator. The study employs ARIMA technique to forecast electrical load, and also the analyzes the ARCH and GARCH effects on the residual time-series.

Files

11311693.pdf

(.pdf | 0.67 Mb)