A hybrid one-step-Ahead time series model based on GA-SVR and EMD for forecasting electricity loads

被引：4

作者：

Shiu M.-C. ^{[1
]}

Wei L.-Y. ^{[2
]}

Liu J.-W. ^{[3
]}

Huang D.-Y. ^{[2
]}

Tu C.-C. ^{[2
]}

Liao K.-H. ^{[2
]}

机构：

[1] Department of Information Management, National Yunlin University of Science and Technology, Yunlin

[2] Department of Information Management, Yuanpei University of Medical Technology, Hsinchu

[3] Department of Sport Information and Communication, National Taiwan University of Sport, Taichung

来源：

Wei, Liang-Ying (lywei@mail.ypu.edu.tw) | 1600年 / 20期

关键词：

Electricity load; Empirical mode decomposition; Genetic algorithm; One-step-Ahead method; Support vector regression; Time series;

D O I：

10.6180/jase.2017.20.4.08

中图分类号：

学科分类号：

摘要：

Economic growth increases the demand for electricity, and forecasting electricity loads is critical for providing cheaper electricity. Conventional time series methods have been applied to forecast electricity loads. However, traditional statistical methods, such as regression models, are unable to address nonlinear relationships, such as those of electricity loads. Moreover, most time series models which use electricity load data with many factors, such as climate conditions and region environments, involutedly would reduce the forecasting performance. To overcome these problems and improve the forecasting ability of time series models, this paper proposes a hybrid one-step-Ahead time series model that is based on support vector regression (SVR), empirical mode decomposition (EMD), and a genetic algorithm (GA) to predict electricity loads. The experimental results were generated from 2 electricity load datasets from various countries, and the proposed model was compared with several models. Our findings indicate that the proposed model outperforms the other approaches in terms of mean absolute percentage error (MAPE).

引用

页码：467 / 476

页数：9

共 47 条

[1]

Cheng C.H., Wei L.Y., One step-Ahead ANFIS time series model for forecasting electricity loads, Optimization and Engineering, 11, pp. 303-317, (2010)

[2]

Nowak M.P., Schultz R., Westphalen A., A stochastic integer programming model for incorporating day-Ahead trading of into hydro-Thermal unit commitment, Optim Eng, 6, pp. 163-176, (2005)

[3]

Rnberg R.N., Misch W.R., A two-stage planning model for power scheduling in a hydro-Thermal system under uncertainty, Optim Eng, 3, pp. 355-378, (2002)

[4]

Bunn D.W., Farmer E.D., Comparative Models for Electrical Load Forecasting, (1985)

[5]

Yang Y., Chen Y., Wang Y., Li C., Li L., Modelling a combined method based on ANFIS and neural network improved by DE Algorithm: A case study for short-Term electricity demand forecasting, Applied Soft Computing, 49, pp. 663-675, (2016)

[6]

Koprinska I., Rana M., Agelidis V.G., Correlation and instance based feature selection for electricity load forecasting, Knowledge-Based Systems, 82, pp. 29-40, (2015)

[7]

Friedrich L., Armstrong P., Afshari A., Midterm forecasting of urban electricity load to isolate air-conditioning impact, Energy and Buildings, 80, pp. 72-80, (2014)

[8]

Huang S.J., Shih K.R., Short-Term load forecasting via ARMA model identification including non-Gaussian process considerations, IEEE Trans Power Syst, 18, 2, pp. 673-679, (2003)

[9]

Haida T., Muto S., Regression based peak load forecasting using a transformation technique, IEEE Trans Power Syst, 9, pp. 1788-1794, (1994)

[10]

Pino R., Parreno J., Gomez A., Priore P., Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks, Engineering Applications of Artificial Intelligence, 21, pp. 53-62, (2008)

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