Real-time Electricity Market Price Prediction using Improved ARIMA Model

Hussein Abdellatif; Alaa E. Hussein; Ali T. Alawami; Mohammad A. Abido

doi:10.1109/EEEIC/ICPSEurope57605.2023.10194648

Real-time Electricity Market Price Prediction using Improved ARIMA Model

Hussein Abdellatif, Alaa E. Hussein, Ali T. Alawami, Mohammad A. Abido

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

A virtual power plant (VPP) is analogous to a microgrid. However, a VPP is designed to participate in the electricity markets, such as the day-ahead (DA) and the real-time (RT) markets, while a microgrid does not. In order to participate in the electricity markets, historical data evaluation is necessary to predict market behavior. There are three main parameters to predict: the day-ahead market price, the real-time market price, and the renewable energy sources data such as the wind speed. One of the popular techniques used for forecasting is the autoregressive integrated moving average (ARIMA) model. In this paper, an improved ARIMA model is introduced. Using the proposed sequential ARIMA model, the VPP operator can forecast the uncertain parameters with higher accuracy, improving the optimization decisions taken by the VPP. This will reduce the risk of failure to participate in the electricity markets and give a better opportunity to achieve better results in terms of the optimization objectives such as cost minimization and profit maximization.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023
Editors	Zbigniew Leonowicz
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350347432
DOIs	https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194648
State	Published - 2023
Event	2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 - Madrid, Spain Duration: 6 Jun 2023 → 9 Jun 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

Conference

Conference	2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023
Country/Territory	Spain
City	Madrid
Period	6/06/23 → 9/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

ARIMA
MILP
Sequential ARIMA
Stochastic Scheduling
VPP

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality
Environmental Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EEEIC/ICPSEurope57605.2023.10194648

Cite this

Abdellatif, H., Hussein, A. E., Alawami, A. T., & Abido, M. A. (2023). Real-time Electricity Market Price Prediction using Improved ARIMA Model. In Z. Leonowicz (Ed.), Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 (Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194648

Abdellatif, Hussein ; Hussein, Alaa E. ; Alawami, Ali T. et al. / Real-time Electricity Market Price Prediction using Improved ARIMA Model. Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023. editor / Zbigniew Leonowicz. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023).

@inproceedings{e06c26fad4fd4871bdd1146972f01be8,

title = "Real-time Electricity Market Price Prediction using Improved ARIMA Model",

abstract = "A virtual power plant (VPP) is analogous to a microgrid. However, a VPP is designed to participate in the electricity markets, such as the day-ahead (DA) and the real-time (RT) markets, while a microgrid does not. In order to participate in the electricity markets, historical data evaluation is necessary to predict market behavior. There are three main parameters to predict: the day-ahead market price, the real-time market price, and the renewable energy sources data such as the wind speed. One of the popular techniques used for forecasting is the autoregressive integrated moving average (ARIMA) model. In this paper, an improved ARIMA model is introduced. Using the proposed sequential ARIMA model, the VPP operator can forecast the uncertain parameters with higher accuracy, improving the optimization decisions taken by the VPP. This will reduce the risk of failure to participate in the electricity markets and give a better opportunity to achieve better results in terms of the optimization objectives such as cost minimization and profit maximization.",

keywords = "ARIMA, MILP, Sequential ARIMA, Stochastic Scheduling, VPP",

author = "Hussein Abdellatif and Hussein, \{Alaa E.\} and Alawami, \{Ali T.\} and Abido, \{Mohammad A.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 ; Conference date: 06-06-2023 Through 09-06-2023",

year = "2023",

doi = "10.1109/EEEIC/ICPSEurope57605.2023.10194648",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Zbigniew Leonowicz",

booktitle = "Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023",

address = "United States",

}

Abdellatif, H, Hussein, AE, Alawami, AT & Abido, MA 2023, Real-time Electricity Market Price Prediction using Improved ARIMA Model. in Z Leonowicz (ed.), Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023. Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023, Madrid, Spain, 6/06/23. https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194648

Real-time Electricity Market Price Prediction using Improved ARIMA Model. / Abdellatif, Hussein; Hussein, Alaa E.; Alawami, Ali T. et al.
Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023. ed. / Zbigniew Leonowicz. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Real-time Electricity Market Price Prediction using Improved ARIMA Model

AU - Abdellatif, Hussein

AU - Hussein, Alaa E.

AU - Alawami, Ali T.

AU - Abido, Mohammad A.

PY - 2023

Y1 - 2023

N2 - A virtual power plant (VPP) is analogous to a microgrid. However, a VPP is designed to participate in the electricity markets, such as the day-ahead (DA) and the real-time (RT) markets, while a microgrid does not. In order to participate in the electricity markets, historical data evaluation is necessary to predict market behavior. There are three main parameters to predict: the day-ahead market price, the real-time market price, and the renewable energy sources data such as the wind speed. One of the popular techniques used for forecasting is the autoregressive integrated moving average (ARIMA) model. In this paper, an improved ARIMA model is introduced. Using the proposed sequential ARIMA model, the VPP operator can forecast the uncertain parameters with higher accuracy, improving the optimization decisions taken by the VPP. This will reduce the risk of failure to participate in the electricity markets and give a better opportunity to achieve better results in terms of the optimization objectives such as cost minimization and profit maximization.

AB - A virtual power plant (VPP) is analogous to a microgrid. However, a VPP is designed to participate in the electricity markets, such as the day-ahead (DA) and the real-time (RT) markets, while a microgrid does not. In order to participate in the electricity markets, historical data evaluation is necessary to predict market behavior. There are three main parameters to predict: the day-ahead market price, the real-time market price, and the renewable energy sources data such as the wind speed. One of the popular techniques used for forecasting is the autoregressive integrated moving average (ARIMA) model. In this paper, an improved ARIMA model is introduced. Using the proposed sequential ARIMA model, the VPP operator can forecast the uncertain parameters with higher accuracy, improving the optimization decisions taken by the VPP. This will reduce the risk of failure to participate in the electricity markets and give a better opportunity to achieve better results in terms of the optimization objectives such as cost minimization and profit maximization.

KW - ARIMA

KW - MILP

KW - Sequential ARIMA

KW - Stochastic Scheduling

KW - VPP

UR - https://www.scopus.com/pages/publications/85168652947

U2 - 10.1109/EEEIC/ICPSEurope57605.2023.10194648

DO - 10.1109/EEEIC/ICPSEurope57605.2023.10194648

M3 - Conference contribution

AN - SCOPUS:85168652947

T3 - Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

BT - Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

A2 - Leonowicz, Zbigniew

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

Y2 - 6 June 2023 through 9 June 2023

ER -

Abdellatif H, Hussein AE, Alawami AT, Abido MA. Real-time Electricity Market Price Prediction using Improved ARIMA Model. In Leonowicz Z, editor, Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023). doi: 10.1109/EEEIC/ICPSEurope57605.2023.10194648

Real-time Electricity Market Price Prediction using Improved ARIMA Model

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this