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 17 → 2019 四月 19 |
出版系列
| 名字 | 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings |
|---|
Conference
| Conference | 6th IEEE International Conference on Energy Smart Systems, ESS 2019 |
|---|---|
| 國家 | Ukraine |
| 城市 | Kyiv |
| 期間 | 19-04-17 → 19-04-19 |
指紋
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
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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 -