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Weigang Zhao

Personal Details

First Name:Weigang
Middle Name:
Last Name:Zhao
Suffix:
RePEc Short-ID:pzh667
http://www.ceep.net.cn/english/people/faculty/64847.htm

Affiliation

Center for Energy and Environmental Policy Research (CEEP)
Beijing Institute of Technology

Beijing, China
http://www.ceep.net.cn/

: 86-10-68918651
86-10-68918651

RePEc:edi:cebitcn (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Weigang Zhao & Yunfei Cao & Bo Miao & Ke Wang & Yi-Ming Wei, 2018. "Impacts of shifting China¡¯s final energy consumption to electricity on CO2 emission reduction," CEEP-BIT Working Papers 115, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.

Articles

  1. Zhao, Weigang & Cao, Yunfei & Miao, Bo & Wang, Ke & Wei, Yi-Ming, 2018. "Impacts of shifting China's final energy consumption to electricity on CO2 emission reduction," Energy Economics, Elsevier, vol. 71(C), pages 359-369.
  2. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
  3. Chen Wang & Jie Wu & Jianzhou Wang & Weigang Zhao, 2016. "Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers," Energies, MDPI, Open Access Journal, vol. 9(1), pages 1-19, January.
  4. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
  5. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
  6. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
  7. Zhang, Wenyu & Wu, Jie & Wang, Jianzhou & Zhao, Weigang & Shen, Lin, 2012. "Performance analysis of four modified approaches for wind speed forecasting," Applied Energy, Elsevier, vol. 99(C), pages 324-333.
  8. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.

    Cited by:

    1. Jakub Jurasz & Alexander Kies, 2018. "Day-Ahead Probabilistic Model for Scheduling the Operation of a Wind Pumped-Storage Hybrid Power Station: Overcoming Forecasting Errors to Ensure Reliability of Supply to the Grid," Sustainability, MDPI, Open Access Journal, vol. 10(6), pages 1-21, June.
    2. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    3. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    4. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    5. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, Open Access Journal, vol. 10(5), pages 1-33, May.
    6. Zhao, Weigang & Cao, Yunfei & Miao, Bo & Wang, Ke & Wei, Yi-Ming, 2018. "Impacts of shifting China's final energy consumption to electricity on CO2 emission reduction," Energy Economics, Elsevier, vol. 71(C), pages 359-369.
    7. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting," Applied Energy, Elsevier, vol. 198(C), pages 203-222.

  2. Chen Wang & Jie Wu & Jianzhou Wang & Weigang Zhao, 2016. "Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers," Energies, MDPI, Open Access Journal, vol. 9(1), pages 1-19, January.

    Cited by:

    1. Yiyi Zhang & Jiefeng Liu & Hanbo Zheng & Hua Wei & Ruijin Liao, 2017. "Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model," Energies, MDPI, Open Access Journal, vol. 10(11), pages 1-17, November.
    2. Ruohan Gong & Jiangjun Ruan & Jingzhou Chen & Yu Quan & Jian Wang & Cihan Duan, 2017. "Analysis and Experiment of Hot-Spot Temperature Rise of 110 kV Three-Phase Three-Limb Transformer," Energies, MDPI, Open Access Journal, vol. 10(8), pages 1-12, July.
    3. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, Open Access Journal, vol. 11(1), pages 1-26, January.
    4. Feng Yang & Lin Du & Lijun Yang & Chao Wei & Youyuan Wang & Liman Ran & Peng He, 2018. "A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements," Energies, MDPI, Open Access Journal, vol. 11(3), pages 1-17, March.
    5. Jiefeng Liu & Hanbo Zheng & Yiyi Zhang & Hua Wei & Ruijin Liao, 2017. "Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement," Energies, MDPI, Open Access Journal, vol. 10(10), pages 1-16, October.
    6. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, Open Access Journal, vol. 11(2), pages 1-12, February.
    7. Liang Zou & Yongkang Guo & Han Liu & Li Zhang & Tong Zhao, 2017. "A Method of Abnormal States Detection Based on Adaptive Extraction of Transformer Vibro-Acoustic Signals," Energies, MDPI, Open Access Journal, vol. 10(12), pages 1-18, December.
    8. Lingjie Sun & Yingyi Liu & Boyang Zhang & Yuwei Shang & Haiwen Yuan & Zhao Ma, 2016. "An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers," Energies, MDPI, Open Access Journal, vol. 9(9), pages 1-22, August.

  3. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.

    Cited by:

    1. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    2. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-34, August.
    3. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, Open Access Journal, vol. 9(6), pages 1-16, May.
    4. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    5. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    6. Matsumoto, Ken׳ichi & Andriosopoulos, Kostas, 2016. "Energy security in East Asia under climate mitigation scenarios in the 21st century," Omega, Elsevier, vol. 59(PA), pages 60-71.
    7. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-16, October.
    8. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, Open Access Journal, vol. 11(4), pages 1-34, March.
    9. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    10. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    11. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    12. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-22, November.
    13. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    14. Weide Li & Xuan Yang & Hao Li & Lili Su, 2017. "Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting," Energies, MDPI, Open Access Journal, vol. 10(1), pages 1-17, January.
    15. Xiao, Liye & Wang, Jianzhou & Hou, Ru & Wu, Jie, 2015. "A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting," Energy, Elsevier, vol. 82(C), pages 524-549.
    16. Zhao, Weigang & Cao, Yunfei & Miao, Bo & Wang, Ke & Wei, Yi-Ming, 2018. "Impacts of shifting China's final energy consumption to electricity on CO2 emission reduction," Energy Economics, Elsevier, vol. 71(C), pages 359-369.
    17. Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2015. "A comparative study on prediction methods for China's medium- and long-term coal demand," Energy, Elsevier, vol. 93(P2), pages 1671-1683.
    18. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.

  4. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.

    Cited by:

    1. Afanasyev, Dmitriy & Fedorova, Elena, 2015. "The long-term trends on Russian electricity market: comparison of empirical mode and wavelet decompositions," MPRA Paper 62391, University Library of Munich, Germany.
    2. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    3. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    4. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    5. Tascikaraoglu, A. & Erdinc, O. & Uzunoglu, M. & Karakas, A., 2014. "An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units," Applied Energy, Elsevier, vol. 119(C), pages 445-453.
    6. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    7. Kisi, Ozgur, 2014. "Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach," Energy, Elsevier, vol. 64(C), pages 429-436.
    8. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    9. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    10. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    11. Chahkoutahi, Fatemeh & Khashei, Mehdi, 2017. "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting," Energy, Elsevier, vol. 140(P1), pages 988-1004.
    12. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    13. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    14. Dadkhah, Mojtaba & Jahangoshai Rezaee, Mustafa & Zare Chavoshi, Ahmad, 2018. "Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed," Energy, Elsevier, vol. 148(C), pages 775-788.
    15. Sahraei-Ardakani, Mostafa & Blumsack, Seth & Kleit, Andrew, 2015. "Estimating zonal electricity supply curves in transmission-constrained electricity markets," Energy, Elsevier, vol. 80(C), pages 10-19.
    16. Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
    17. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
    18. Alonso, J. & Batlles, F.J., 2014. "Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery," Energy, Elsevier, vol. 73(C), pages 890-897.
    19. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    20. Chen, Kunlong & Jiang, Jiuchun & Zheng, Fangdan & Chen, Kunjin, 2018. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning," Energy, Elsevier, vol. 150(C), pages 49-60.
    21. He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.
    22. Alonso, J. & Batlles, F.J. & López, G. & Ternero, A., 2014. "Sky camera imagery processing based on a sky classification using radiometric data," Energy, Elsevier, vol. 68(C), pages 599-608.
    23. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    24. Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-11, November.
    25. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2016. "The long-term trends on the electricity markets: Comparison of empirical mode and wavelet decompositions," Energy Economics, Elsevier, vol. 56(C), pages 432-442.
    26. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.

  5. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.

    Cited by:

    1. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. Wei Sun & Mohan Liu & Yi Liang, 2015. "Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm," Energies, MDPI, Open Access Journal, vol. 8(7), pages 1-23, June.
    3. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    4. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2016. "Anomalous volatility scaling in high frequency financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 434-445.
    5. Nantian Huang & Enkai Xing & Guowei Cai & Zhiyong Yu & Bin Qi & Lin Lin, 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection," Energies, MDPI, Open Access Journal, vol. 11(7), pages 1-19, June.
    6. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    7. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    8. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    9. Tascikaraoglu, A. & Erdinc, O. & Uzunoglu, M. & Karakas, A., 2014. "An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units," Applied Energy, Elsevier, vol. 119(C), pages 445-453.
    10. Alonzo, Bastien & Ringkjob, Hans-Kristian & Jourdier, Benedicte & Drobinski, Philippe & Plougonven, Riwal & Tankov, Peter, 2017. "Modelling the variability of the wind energy resource on monthly and seasonal timescales," Renewable Energy, Elsevier, vol. 113(C), pages 1434-1446.
    11. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, Open Access Journal, vol. 5(2), pages 1-16, February.
    12. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    13. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    14. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    15. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, Open Access Journal, vol. 8(8), pages 1-21, August.
    16. Jujie Wang & Yanfeng Wang & Yaning Li, 2018. "A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction," Energies, MDPI, Open Access Journal, vol. 11(2), pages 1-33, February.
    17. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    18. Naik, Jyotirmayee & Dash, Sujit & Dash, P.K. & Bisoi, Ranjeeta, 2018. "Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network," Renewable Energy, Elsevier, vol. 118(C), pages 180-212.
    19. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    20. Feiyu Zhang & Yuqi Dong & Kequan Zhang, 2016. "A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China," Sustainability, MDPI, Open Access Journal, vol. 8(6), pages 1-20, June.
    21. Samet, Haidar & Marzbani, Fatemeh, 2014. "Quantizing the deterministic nonlinearity in wind speed time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1143-1154.
    22. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    23. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
    24. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    25. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    26. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    27. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
    28. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    29. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    30. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    31. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    32. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    33. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, Open Access Journal, vol. 9(2), pages 1-15, February.
    34. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    35. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, Open Access Journal, vol. 10(7), pages 1-29, July.
    36. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    37. Lin, Chiun-Sin & Chiu, Sheng-Hsiung & Lin, Tzu-Yu, 2012. "Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting," Economic Modelling, Elsevier, vol. 29(6), pages 2583-2590.
    38. Erdong Zhao & Jing Zhao & Liwei Liu & Zhongyue Su & Ning An, 2015. "Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation," Energies, MDPI, Open Access Journal, vol. 9(1), pages 1-20, December.
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  6. Zhang, Wenyu & Wu, Jie & Wang, Jianzhou & Zhao, Weigang & Shen, Lin, 2012. "Performance analysis of four modified approaches for wind speed forecasting," Applied Energy, Elsevier, vol. 99(C), pages 324-333.

    Cited by:

    1. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    2. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
    3. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    4. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    5. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    6. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    7. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, Open Access Journal, vol. 9(2), pages 1-15, February.
    8. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    9. Zhiwei Shen & Matthias Ritter, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers SFB649DP2015-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    11. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    12. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    13. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    14. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.

  7. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.

    Cited by:

    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    2. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    3. Jiang, Ping & Liu, Feng & Song, Yiliao, 2017. "A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting," Energy, Elsevier, vol. 119(C), pages 694-709.
    4. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    5. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    6. Maria J. Herrerias & Eric Girardin, 2013. "Seasonal Patterns of Energy in China," Post-Print hal-01499617, HAL.
    7. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    8. Wai-Ming To & Peter Ka Chun Lee & Tsz-Ming Lai, 2017. "Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong," Energies, MDPI, Open Access Journal, vol. 10(7), pages 1-16, June.
    9. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    10. Chahkoutahi, Fatemeh & Khashei, Mehdi, 2017. "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting," Energy, Elsevier, vol. 140(P1), pages 988-1004.
    11. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    12. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    13. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    14. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    15. Tang, Ling & Yu, Lean & He, Kaijian, 2014. "A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 128(C), pages 1-14.
    16. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    17. Weide Li & Xuan Yang & Hao Li & Lili Su, 2017. "Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting," Energies, MDPI, Open Access Journal, vol. 10(1), pages 1-17, January.
    18. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    19. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.

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