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Erschienen in: Energy Systems 4/2019

15.08.2018 | Original Paper

China’s energy consumption prediction considering error correction based on decompose–ensemble method

verfasst von: Cheng Zhou, Xiyang Chen

Erschienen in: Energy Systems | Ausgabe 4/2019

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Abstract

Energy consumption forecasting plays a vital role in rational energy and economic planning formulation for a country or an area around the world. A novel forecasting method based on error correction and decompose-ensemble strategy combined with linear regression model (LR) and triple exponential smoothing model (TESM) is proposed in this study. Firstly, original energy consumption is decomposed into the main trend subseries and non-stationary errors subseries by use of LR model. And the mainly trend subseries is forecasted by LR. Then, the non-stationary subseries (forecasting errors of LR) is decomposed into several intrinsic mode functions (high-frequency subseries, middle-frequency subseries and low-frequency subseries) and residual subseries by empirical mode decomposition (EMD). With respect to their different dynamic changing features and influenced factors, each intrinsic mode functions subseries and residual subseries are forecasted, respectively by TESM. Finally, the prediction of energy consumption is obtained by summing the trend subseries prediction results and these errors subseries prediction results. Forecasting results prove that error correction is a useful strategy for improve the forecasting performance. Most of all, the origin complex errors correction forecasting problem has been resolved into some simple forecasting problem based on decompose-ensemble strategy, which have better forecasting performance, compared with individual models (LR, auto regression model (AR) and TESM), traditional error correction method, combination models (which is developed by using of LR, AR and TESM based on equal weight method, entropy weight method and optimal weight method). The proposed novel method can provide accurate and reliable forecasting results, which is a feasible forecasting approach for China’s annual energy consumption. By use of the proposed forecasting method considering error correction based on EMD decompose-ensemble strategy combined with LR and TESM, China’s energy consumption in 2021 will increase to 484,555.30 ten thousand standard tons coal equivalent (tce), and the average annual growth of the coming 5 years is 2.135%. Since China’s energy consumption is still on its growing process, China should pay more attention to change its economic development model from energy-intensive economy to low-carbon economy.

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Literatur
1.
Zurück zum Zitat Kankal, M., Akpınar, A., Kömürcü, M.I., et al.: Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Appl. Energy 88, 1927–1939 (2011)CrossRef Kankal, M., Akpınar, A., Kömürcü, M.I., et al.: Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Appl. Energy 88, 1927–1939 (2011)CrossRef
2.
Zurück zum Zitat Utgikar, V.P., Scott, J.P.: Energy forecasting: predictions, reality and analysis of causes of error. Energy Policy 34, 3087–3092 (2006)CrossRef Utgikar, V.P., Scott, J.P.: Energy forecasting: predictions, reality and analysis of causes of error. Energy Policy 34, 3087–3092 (2006)CrossRef
3.
Zurück zum Zitat Lee, Y.S., Tong, L.I.: Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers. Manage. 52, 47–152 (2011) Lee, Y.S., Tong, L.I.: Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers. Manage. 52, 47–152 (2011)
4.
Zurück zum Zitat Yu, S., Wei, Y.M., Ke, W.: China’s primary energy demands in 2020: predictions from an MPSO–RBF estimation model. Energy Convers. Manage. 61, 59–66 (2012)CrossRef Yu, S., Wei, Y.M., Ke, W.: China’s primary energy demands in 2020: predictions from an MPSO–RBF estimation model. Energy Convers. Manage. 61, 59–66 (2012)CrossRef
5.
Zurück zum Zitat Fattaheian-Dehkordi, S., Fereidunian, A., Gholami-Dehkordi, H., Lesani, H.: Hour-ahead demand forecasting in smart grid using support vector regression (SVR). Int. Trans. Electr. Energy Syst 24, 1650–1663 (2014)CrossRef Fattaheian-Dehkordi, S., Fereidunian, A., Gholami-Dehkordi, H., Lesani, H.: Hour-ahead demand forecasting in smart grid using support vector regression (SVR). Int. Trans. Electr. Energy Syst 24, 1650–1663 (2014)CrossRef
6.
Zurück zum Zitat Gürbüz, F., Öztürk, C., Pardalos, P.: Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study. Energy Syst 4, 289–300 (2013)CrossRef Gürbüz, F., Öztürk, C., Pardalos, P.: Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study. Energy Syst 4, 289–300 (2013)CrossRef
7.
Zurück zum Zitat Yuan, C., Liu, S., Fang, Z.: Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model. Energy 100, 384–390 (2016)CrossRef Yuan, C., Liu, S., Fang, Z.: Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model. Energy 100, 384–390 (2016)CrossRef
8.
Zurück zum Zitat Xie, N.M., Yuan, C.Q., Yang, Y.J.: Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model. Int. J. Electr. Power Energy Syst. 66, 1–8 (2015)CrossRef Xie, N.M., Yuan, C.Q., Yang, Y.J.: Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model. Int. J. Electr. Power Energy Syst. 66, 1–8 (2015)CrossRef
9.
Zurück zum Zitat Oğcu, G., Demirel, O.F., Zaim, S.: Forecasting electricity consumption with neural networks and support vector regression. Proc. Soc. Behav. Sci. 58, 1576–1585 (2012)CrossRef Oğcu, G., Demirel, O.F., Zaim, S.: Forecasting electricity consumption with neural networks and support vector regression. Proc. Soc. Behav. Sci. 58, 1576–1585 (2012)CrossRef
10.
Zurück zum Zitat Barak, S., Sadegh, S.S.: Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. Int. J. Electr. Power Energy Syst. 82, 92–104 (2016)CrossRef Barak, S., Sadegh, S.S.: Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. Int. J. Electr. Power Energy Syst. 82, 92–104 (2016)CrossRef
11.
Zurück zum Zitat Lee, Y.S., Tong, L.I.: Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl. Energy 94, 251–256 (2012)CrossRef Lee, Y.S., Tong, L.I.: Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl. Energy 94, 251–256 (2012)CrossRef
12.
Zurück zum Zitat Zhou, C., Chen, X.Y.: Adaptive combination forecasting model based on area correlation degree with application to China’s energy consumption. J. Appl. Math. 1–12 (2014) Zhou, C., Chen, X.Y.: Adaptive combination forecasting model based on area correlation degree with application to China’s energy consumption. J. Appl. Math. 1–12 (2014)
13.
Zurück zum Zitat Yu, S., Wei, Y.M., Wang, K.: A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy 42, 329–340 (2012)CrossRef Yu, S., Wei, Y.M., Wang, K.: A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy 42, 329–340 (2012)CrossRef
14.
Zurück zum Zitat Atsalakis, G., Frantzis, D., Zopounidis, C.: Energy’s exports forecasting by a neuro-fuzzy controller. Energy Syst 6, 249–267 (2015)CrossRef Atsalakis, G., Frantzis, D., Zopounidis, C.: Energy’s exports forecasting by a neuro-fuzzy controller. Energy Syst 6, 249–267 (2015)CrossRef
15.
Zurück zum Zitat Liua, X., Cuartas, B.M., Muñiz, A.S.G.: A Grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish Economic sectors. Energy 115, 1042–1054 (2016)CrossRef Liua, X., Cuartas, B.M., Muñiz, A.S.G.: A Grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish Economic sectors. Energy 115, 1042–1054 (2016)CrossRef
16.
Zurück zum Zitat Karimi, H., Dastranj, J.: Artificial neural network-based genetic algorithm to predict natural gas consumption. Energy Syst 5, 571–581 (2014)CrossRef Karimi, H., Dastranj, J.: Artificial neural network-based genetic algorithm to predict natural gas consumption. Energy Syst 5, 571–581 (2014)CrossRef
17.
Zurück zum Zitat Chai, J., Guo, J., Lu, H.: Forecasting energy demand of China using Bayesian Combination model. China Popul. Resour. Environ. 18, 50–55 (2008)CrossRef Chai, J., Guo, J., Lu, H.: Forecasting energy demand of China using Bayesian Combination model. China Popul. Resour. Environ. 18, 50–55 (2008)CrossRef
18.
Zurück zum Zitat Seok, J.H., Kim, J.J., Lee, J.Y., et al.: Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting. Int. J. Control Autom. Syst. 12, 1245–1256 (2014)CrossRef Seok, J.H., Kim, J.J., Lee, J.Y., et al.: Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting. Int. J. Control Autom. Syst. 12, 1245–1256 (2014)CrossRef
19.
Zurück zum Zitat Chen, M., Yuan, J., Liu, D., et al.: An adaption scheduling based on dynamic weighted random forests for load demand forecasting. J. Supercomput. 1–19 (2017) Chen, M., Yuan, J., Liu, D., et al.: An adaption scheduling based on dynamic weighted random forests for load demand forecasting. J. Supercomput. 1–19 (2017)
20.
Zurück zum Zitat Li, G.L., Cai, Z.H., Kang, X.J., Wu, Z.D., Wang, Y.Z.: ESPSA: a prediction-based algorithmfor streaming time series segmentation. Expert Syst. Appl. 41, 6098–6105 (2014)CrossRef Li, G.L., Cai, Z.H., Kang, X.J., Wu, Z.D., Wang, Y.Z.: ESPSA: a prediction-based algorithmfor streaming time series segmentation. Expert Syst. Appl. 41, 6098–6105 (2014)CrossRef
21.
Zurück zum Zitat Bindiu, R., Chindri, M., Pop, G.V.: Day-ahead load forecasting using exponential smoothing. Sci. Bull. Petru Maior Univ. Tirgu Mures 6, 89–93 (2009) Bindiu, R., Chindri, M., Pop, G.V.: Day-ahead load forecasting using exponential smoothing. Sci. Bull. Petru Maior Univ. Tirgu Mures 6, 89–93 (2009)
22.
Zurück zum Zitat Ma, X., Hu, J., Zhang, L.: EMD-based online filtering of process data. Control Eng. Pract. 62, 79–91 (2017)CrossRef Ma, X., Hu, J., Zhang, L.: EMD-based online filtering of process data. Control Eng. Pract. 62, 79–91 (2017)CrossRef
23.
Zurück zum Zitat Wei, L.Y.: A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput. 42, 368–376 (2016)CrossRef Wei, L.Y.: A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput. 42, 368–376 (2016)CrossRef
24.
Zurück zum Zitat Kopsinis, Y., Mclaughlin, S.: Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans. Signal Process. 57(4), 1351–1362 (2009)MathSciNetCrossRef Kopsinis, Y., Mclaughlin, S.: Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans. Signal Process. 57(4), 1351–1362 (2009)MathSciNetCrossRef
Metadaten
Titel
China’s energy consumption prediction considering error correction based on decompose–ensemble method
verfasst von
Cheng Zhou
Xiyang Chen
Publikationsdatum
15.08.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 4/2019
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-018-0300-1

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