A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State

K. W. Yu, C. H. Hsu, Shih-Ming Yang

研究成果: Conference contribution

摘要

This paper proposes a model having both linear and nonlinear system dynamics by integrating both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model to simulate electrical energy supply inherent with strong seasonal and periodic characteristics in power system. Accurate electrical load forecast becomes possible by the integrated model for the ARIMA is effective to electricity load time series inherent with seasonal fluctuations as well as strong 7-day (per week) periodic characteristics. By using the input of historical daily electricity load data, weather data, and holiday effect variables, the integrated model is shown to be more accurate than the ANN model, the ARIMA model, the classical ARIMA-ANN model, and other well-known methods in the prediction and the forecast of electrical load in normal summer week, normal winter week, 3/4-day holiday week, long holiday week, and extreme weather week.

原文English
主出版物標題2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面19-24
頁數6
ISBN(電子)9781728121598
DOIs
出版狀態Published - 2019 四月 1
事件6th IEEE International Conference on Energy Smart Systems, ESS 2019 - Kyiv, Ukraine
持續時間: 2019 四月 172019 四月 19

出版系列

名字2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings

Conference

Conference6th IEEE International Conference on Energy Smart Systems, ESS 2019
國家Ukraine
城市Kyiv
期間19-04-1719-04-19

指紋

Dynamic loads
Electricity
Neural networks
Linear systems
Nonlinear systems
Time series

All Science Journal Classification (ASJC) codes

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

引用此文

Yu, K. W., Hsu, C. H., & Yang, S-M. (2019). A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State. 於 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings (頁 19-24). [8764179] (2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ESS.2019.8764179
Yu, K. W. ; Hsu, C. H. ; Yang, Shih-Ming. / A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State. 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 頁 19-24 (2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings).
@inproceedings{711f8c2fd4d2417687c89be7073e8083,
title = "A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State",
abstract = "This paper proposes a model having both linear and nonlinear system dynamics by integrating both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model to simulate electrical energy supply inherent with strong seasonal and periodic characteristics in power system. Accurate electrical load forecast becomes possible by the integrated model for the ARIMA is effective to electricity load time series inherent with seasonal fluctuations as well as strong 7-day (per week) periodic characteristics. By using the input of historical daily electricity load data, weather data, and holiday effect variables, the integrated model is shown to be more accurate than the ANN model, the ARIMA model, the classical ARIMA-ANN model, and other well-known methods in the prediction and the forecast of electrical load in normal summer week, normal winter week, 3/4-day holiday week, long holiday week, and extreme weather week.",
author = "Yu, {K. W.} and Hsu, {C. H.} and Shih-Ming Yang",
year = "2019",
month = "4",
day = "1",
doi = "10.1109/ESS.2019.8764179",
language = "English",
series = "2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "19--24",
booktitle = "2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings",
address = "United States",

}

Yu, KW, Hsu, CH & Yang, S-M 2019, A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State. 於 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings., 8764179, 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 頁 19-24, 6th IEEE International Conference on Energy Smart Systems, ESS 2019, Kyiv, Ukraine, 19-04-17. https://doi.org/10.1109/ESS.2019.8764179

A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State. / Yu, K. W.; Hsu, C. H.; Yang, Shih-Ming.

2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 19-24 8764179 (2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings).

研究成果: Conference contribution

TY - GEN

T1 - A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State

AU - Yu, K. W.

AU - Hsu, C. H.

AU - Yang, Shih-Ming

PY - 2019/4/1

Y1 - 2019/4/1

N2 - This paper proposes a model having both linear and nonlinear system dynamics by integrating both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model to simulate electrical energy supply inherent with strong seasonal and periodic characteristics in power system. Accurate electrical load forecast becomes possible by the integrated model for the ARIMA is effective to electricity load time series inherent with seasonal fluctuations as well as strong 7-day (per week) periodic characteristics. By using the input of historical daily electricity load data, weather data, and holiday effect variables, the integrated model is shown to be more accurate than the ANN model, the ARIMA model, the classical ARIMA-ANN model, and other well-known methods in the prediction and the forecast of electrical load in normal summer week, normal winter week, 3/4-day holiday week, long holiday week, and extreme weather week.

AB - This paper proposes a model having both linear and nonlinear system dynamics by integrating both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model to simulate electrical energy supply inherent with strong seasonal and periodic characteristics in power system. Accurate electrical load forecast becomes possible by the integrated model for the ARIMA is effective to electricity load time series inherent with seasonal fluctuations as well as strong 7-day (per week) periodic characteristics. By using the input of historical daily electricity load data, weather data, and holiday effect variables, the integrated model is shown to be more accurate than the ANN model, the ARIMA model, the classical ARIMA-ANN model, and other well-known methods in the prediction and the forecast of electrical load in normal summer week, normal winter week, 3/4-day holiday week, long holiday week, and extreme weather week.

UR - http://www.scopus.com/inward/record.url?scp=85069915901&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069915901&partnerID=8YFLogxK

U2 - 10.1109/ESS.2019.8764179

DO - 10.1109/ESS.2019.8764179

M3 - Conference contribution

AN - SCOPUS:85069915901

T3 - 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings

SP - 19

EP - 24

BT - 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Yu KW, Hsu CH, Yang S-M. A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State. 於 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 19-24. 8764179. (2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings). https://doi.org/10.1109/ESS.2019.8764179