Volatility in Electrical Load Forecasting for Long-term Horizon:  An ARIMA-GARCH Approach

Swasti Khuntia; J.L.  Rueda; Mart van der Meijden

doi:10.1109/PMAPS.2016.7764184

Volatility in Electrical Load Forecasting for Long-term Horizon: An ARIMA-GARCH Approach

Swasti Khuntia, J.L. Rueda, Mart van der Meijden

Intelligent Electrical Power Grids

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

69 Downloads (Pure)

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.

Original language	English
Title of host publication	2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Print)	978-1-5090-1970-0
DOIs	https://doi.org/10.1109/PMAPS.2016.7764184
Publication status	Published - 2016
Event	PMAPS 2016: International Conference on Probabilistic Methods Applied to Power Systems - Beijing, China Duration: 16 Oct 2016 → 20 Oct 2016

Conference

Conference	PMAPS 2016
Country/Territory	China
City	Beijing
Period	16/10/16 → 20/10/16

Keywords

ARIMA
ARCH
GARCH
long-term load forecast
volatility

Access to Document

10.1109/PMAPS.2016.7764184

11311693Accepted author manuscript, 573 KB

Cite this

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title = "Volatility in Electrical Load Forecasting for Long-term Horizon: An ARIMA-GARCH Approach",

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.",

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author = "Swasti Khuntia and J.L. Rueda and {van der Meijden}, Mart",

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Volatility in Electrical Load Forecasting for Long-term Horizon: An ARIMA-GARCH Approach. / Khuntia, Swasti; Rueda, J.L.; van der Meijden, Mart.
2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016. Piscataway, NJ: IEEE, 2016. p. 1-6.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AB - 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.

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