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    1.Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China

    Wang, JZ, Huang, XJ,     More...

    ENERGY[0360-5442], Published 2018, Volume 164, Pages 432-448

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 48  2023影響因子:  9.0  发表年影響因子:  5.537 

    2.Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system

    Wang, JZ, Yang, WD,     More...

    ENERGY[0360-5442], Published 2018, Volume 148, Pages 59-78

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 111  2023影響因子:  9.0  发表年影響因子:  5.537 

    3.Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms

    Li, JR, Wang, R, Wan     More...

    ENERGY[0360-5442], Published 2018, Volume 144, Pages 243-264

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 51  2023影響因子:  9.0  发表年影響因子:  5.537 

    4.Research and application of a combined model based on multi objective optimization for multi-step ahead wind speed forecasting

    Wang, JZ, Heng, JN,     More...

    ENERGY[0360-5442], Published 2017, Volume 125, Pages 591-613

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 109  2023影響因子:  9.0  发表年影響因子:  4.968 

    5.Short-term wind speed forecasting using a hybrid model

    Jiang, P, Wang, Y, W     More...

    ENERGY[0360-5442], Published 2017, Volume 119, Pages 561-577

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 102  2023影響因子:  9.0  发表年影響因子:  4.968 

    6.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 Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

    Wang, JZ, Hu, JM

    ENERGY[0360-5442], Published 2015, Volume 93, Pages 41-56

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 239  2023影響因子:  9.0  发表年影響因子:  4.292 

    7.Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression

    Hu, JM, Wang, JZ

    ENERGY[0360-5442], Published 2015, Volume 93, Pages 1456-1466

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 167  2023影響因子:  9.0  发表年影響因子:  4.292 

    8.A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China

    Wang, Y, Wang, JZ, W     More...

    ENERGY[0360-5442], Published 2015, Volume 91, Pages 556-572

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 113  2023影響因子:  9.0  发表年影響因子:  4.292 

    9.A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting

    Xiao, LY, Wang, JZ,     More...

    ENERGY[0360-5442], Published 2015, Volume 82, Pages 524-549

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 113  2023影響因子:  9.0  发表年影響因子:  4.292 

    10.A hybrid technique for short-term wind speed prediction

    Hu, JM, Wang, JZ, Ma     More...

    ENERGY[0360-5442], Published 2015, Volume 81, Pages 563-574

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 107  2023影響因子:  9.0  发表年影響因子:  4.292 

    11.Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm

    Wang, JZ, Jiang, H,     More...

    ENERGY[0360-5442], Published 2015, Volume 81, Pages 627-644

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 80  2023影響因子:  9.0  发表年影響因子:  4.292 

    12.A hybrid forecasting model based on outlier detection and fuzzy time series - A case study on Hainan wind farm of China

    Wang, JZ, Xiong, SH

    ENERGY[0360-5442], Published 2014, Volume 76, Pages 526-541

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 54  2023影響因子:  9.0  发表年影響因子:  4.844 

    13.Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting

    An, N, Zhao, WG, Wan     More...

    ENERGY[0360-5442], Published 2013, Volume 49, Issue 1, Pages 279-288

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 156  2023影響因子:  9.0  发表年影響因子:  4.159 

    14.An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting

    Che, JX, Wang, JZ, W     More...

    ENERGY[0360-5442], Published 2012, Volume 37, Issue 1, Pages 657-664

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 90  2023影響因子:  9.0  发表年影響因子:  3.651 

    15.A corrected hybrid approach for wind speed prediction in Hexi Corridor of China

    Guo, ZH, Zhao, J, Zh     More...

    ENERGY[0360-5442], Published 2011, Volume 36, Issue 3, Pages 1668-1679

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 127  2023影響因子:  9.0  发表年影響因子:  3.487 

    16.An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

    Cai, YA, Wang, JZ, T     More...

    ENERGY[0360-5442], Published 2011, Volume 36, Issue 2, Pages 1340-1350

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 23  2023影響因子:  9.0  发表年影響因子:  3.487 

    17.Combined modeling for electric load forecasting with adaptive particle swarm optimization

    Wang, JZ, Zhu, SL, Z     More...

    ENERGY[0360-5442], Published 2010, Volume 35, Issue 4, Pages 1671-1678

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 114  2023影響因子:  9.0  发表年影響因子:  3.597 

    18.A novel combined model for wind speed prediction - Combination of linear model, shallow neural networks, and deep learning approaches

    Wang, S, Wang, JZ, L     More...

    ENERGY[0360-5442], Published 2021, Volume 234,

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 73  2023影響因子:  9.0  发表年影響因子:  8.857 

    19.Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction

    Wang, Y, Wang, JZ, L     More...

    ENERGY[0360-5442], Published 2021, Volume 231,

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 48  2023影響因子:  9.0  发表年影響因子:  8.857 

    20.A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization

    Guo, HG, Wang, JZ, L     More...

    ENERGY[0360-5442], Published 2022, Volume 239,

    收錄情况: WOS SCOPUS

    WOS核心合集引用: 27  2023影響因子:  9.0  发表年影響因子:  8.9 

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