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洪維強
(Wei-Chiang Samuelson Hong)

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.

Articles

  1. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.

    Cited by:

    1. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    2. Tonglin Fu & Xinrong Li, 2020. "A Combination Forecasting Strategy for Precipitation, Temperature and Wind Speed in the Southeastern Margin of the Tengger Desert," Sustainability, MDPI, vol. 12(4), pages 1-22, February.
    3. Radhakrishnan Angamuthu Chinnathambi & Anupam Mukherjee & Mitch Campion & Hossein Salehfar & Timothy M. Hansen & Jeremy Lin & Prakash Ranganathan, 2018. "A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets," Forecasting, MDPI, vol. 1(1), pages 1-21, July.
    4. Xin-gang, Zhao & Ze-qi, Zhang & Yi-min, Xie & Jin, Meng, 2020. "Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization," Energy, Elsevier, vol. 195(C).
    5. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    6. Yuval Beck & Ram Machlev, 2019. "Harmonic Loads Classification by Means of Currents’ Physical Components," Energies, MDPI, vol. 12(21), pages 1-18, October.
    7. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    8. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
    9. Taosheng Wang & Hongyan Zuo & C. H. Wu & B. Hu, 2021. "Combined soft measurement on key indicator parameters of new competitive advantages for China's export," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    10. Hugo Algarvio & António Couto & Fernando Lopes & Ana Estanqueiro, 2019. "Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study," Energies, MDPI, vol. 12(14), pages 1-20, July.
    11. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    12. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    13. Guo-Feng Fan & Yan-Hui Guo & Jia-Mei Zheng & Wei-Chiang Hong, 2019. "Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(5), pages 1-19, March.
    14. Gang Chen & Qingchang Hu & Jin Wang & Xu Wang & Yuyu Zhu, 2023. "Machine-Learning-Based Electric Power Forecasting," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    15. Cheng-Hong Yang & Po-Yin Chang, 2020. "Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    16. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    17. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    18. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    19. Mohanad S. Al-Musaylh & Ravinesh C. Deo & Yan Li, 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms," Energies, MDPI, vol. 13(9), pages 1-19, May.
    20. Wang, Xiao & Sun, Xiao-Xue & Chu, Shu-Chuan & Watada, Junzo & Pan, Jeng-Shyang, 2023. "Improved butterfly optimization algorithm applied to prediction of combined cycle power plant," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 337-353.
    21. Bin Li & Mingzhen Lu & Yiyi Zhang & Jia Huang, 2019. "A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction," Energies, MDPI, vol. 12(20), pages 1-19, October.
    22. Tongxiang Liu & Yu Jin & Yuyang Gao, 2019. "A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization," Energies, MDPI, vol. 12(8), pages 1-20, April.
    23. Mohamed Massaoudi & Shady S. Refaat & Haitham Abu-Rub & Ines Chihi & Fakhreddine S. Oueslati, 2020. "PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-29, October.
    24. Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.
    25. Michal Pavlicko & Mária Vojteková & Oľga Blažeková, 2022. "Forecasting of Electrical Energy Consumption in Slovakia," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    26. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).

  2. Ming-Wei Li & Jing Geng & Shumei Wang & Wei-Chiang Hong, 2017. "Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting," Energies, MDPI, vol. 10(12), pages 1-18, December.

    Cited by:

    1. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    2. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    3. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
    4. Ming-Wei Li & Jing Geng & Wei-Chiang Hong & Yang Zhang, 2018. "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting," Energies, MDPI, vol. 11(9), pages 1-22, August.
    5. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.

  3. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.

    Cited by:

    1. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
    2. Li, Jingrui & Wang, Jiyang & Li, Zhiwu, 2023. "A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed," Energy, Elsevier, vol. 264(C).
    3. Dong, Yingchao & Zhang, Hongli & Wang, Cong & Zhou, Xiaojun, 2021. "A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting," Applied Energy, Elsevier, vol. 286(C).
    4. Longo, Alessandro & Bianchi, Stefano & Plastino, Wolfango, 2019. "tvf-EMD based time series analysis of 7Be sampled at the CTBTO-IMS network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 908-914.
    5. Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    6. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    7. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.
    8. Pin Li & Jin-Suo Zhang, 2018. "A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost," Energies, MDPI, vol. 11(7), pages 1-28, June.
    9. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    10. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.
    11. Wenlong Fu & Kai Wang & Jianzhong Zhou & Yanhe Xu & Jiawen Tan & Tie Chen, 2019. "A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
    12. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.

  4. Guo-Feng Fan & Li-Ling Peng & Xiangjun Zhao & Wei-Chiang Hong, 2017. "Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model," Energies, MDPI, vol. 10(11), pages 1-22, October.

    Cited by:

    1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    2. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
    3. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
    4. Robertas Lukočius & Žilvinas Nakutis & Vytautas Daunoras & Ramūnas Deltuva & Pranas Kuzas & Roma Račkienė, 2018. "An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids," Energies, MDPI, vol. 12(1), pages 1-26, December.
    5. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    6. Mohan, Neethu & Soman, K.P. & Sachin Kumar, S., 2018. "A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model," Applied Energy, Elsevier, vol. 232(C), pages 229-244.
    7. Ming-Wei Li & Jing Geng & Wei-Chiang Hong & Yang Zhang, 2018. "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting," Energies, MDPI, vol. 11(9), pages 1-22, August.
    8. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    9. Yuansheng Huang & Lei Yang & Shijian Liu & Guangli Wang, 2019. "Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy," Energies, MDPI, vol. 12(10), pages 1-22, May.
    10. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.
    11. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    12. Guo-Feng Fan & An Wang & Wei-Chiang Hong, 2018. "Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting," Energies, MDPI, vol. 11(7), pages 1-21, June.
    13. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    14. Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).

  5. Dongxiao Niu & Yi Liang & Haichao Wang & Meng Wang & Wei-Chiang Hong, 2017. "Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Wei," Energies, MDPI, vol. 10(8), pages 1-21, August.

    Cited by:

    1. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    2. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    3. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.

  6. 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, vol. 9(11), pages 1-22, November.

    Cited by:

    1. Kai Lei & Buqing Ye & Jin Cao & Rui Zhang & Dong Liu, 2017. "Combustion Characteristics of Single Particles from Bituminous Coal and Pine Sawdust in O 2 /N 2 , O 2 /CO 2 , and O 2 /H 2 O Atmospheres," Energies, MDPI, vol. 10(11), pages 1-12, October.
    2. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    3. Wang, Hongxia & Zhang, Junfeng & Fang, Hong, 2017. "Electricity footprint of China’s industrial sectors and its socioeconomic drivers," Resources, Conservation & Recycling, Elsevier, vol. 124(C), pages 98-106.
    4. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.
    5. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    6. Jindamas Sutthichaimethee & Kuskana Kubaha, 2018. "Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    7. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.
    8. Dongxiao Niu & Yi Liang & Haichao Wang & Meng Wang & Wei-Chiang Hong, 2017. "Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Wei," Energies, MDPI, vol. 10(8), pages 1-21, August.
    9. Hongmei Zhao & Yang Xu & Wei-Chiang Hong & Yi Liang & Dandan Zou, 2021. "Smart Evaluation of Green Campus Sustainability Considering Energy Utilization," Sustainability, MDPI, vol. 13(14), pages 1-21, July.

  7. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Correction: Liang, Y., et al. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search. Energies 2016, 9 , 827," Energies, MDPI, vol. 9(12), pages 1-1, December.

    Cited by:

    1. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    2. Yancai Xiao & Ruolan Dai & Guangjian Zhang & Weijia Chen, 2017. "The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT," Energies, MDPI, vol. 10(11), pages 1-13, November.
    3. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Dongxiao Niu & Weibo Zhao & Si Li & Rongjun Chen, 2018. "Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines," Sustainability, MDPI, vol. 10(1), pages 1-11, January.
    5. Yue-Gang Song & Yu-Long Zhou & Ren-Jie Han, 2018. "Neural networks for stock price prediction," Papers 1805.11317, arXiv.org.
    6. Jin-peng Liu & Chang-ling Li, 2017. "The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
    7. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    8. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    9. Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.

  8. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.

    Cited by:

    1. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    2. Yancai Xiao & Ruolan Dai & Guangjian Zhang & Weijia Chen, 2017. "The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT," Energies, MDPI, vol. 10(11), pages 1-13, November.
    3. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Dongxiao Niu & Weibo Zhao & Si Li & Rongjun Chen, 2018. "Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines," Sustainability, MDPI, vol. 10(1), pages 1-11, January.
    5. Yue-Gang Song & Yu-Long Zhou & Ren-Jie Han, 2018. "Neural networks for stock price prediction," Papers 1805.11317, arXiv.org.
    6. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    7. Jin-peng Liu & Chang-ling Li, 2017. "The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
    8. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    9. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.
    10. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    11. Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.

  9. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.

    Cited by:

    1. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
    2. Khondaker Golam Moazzem & Helen Mashiyat Preoty, 2021. "Proposed Power and Energy System Master Plan (PESMP): Perspective on Analytical Frame, Methodology and Influencing Factors on Demand Forecasting," CPD Working Paper 139, Centre for Policy Dialogue (CPD).
    3. Ricardo Vazquez & Hortensia Amaris & Monica Alonso & Gregorio Lopez & Jose Ignacio Moreno & Daniel Olmeda & Javier Coca, 2017. "Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project," Energies, MDPI, vol. 10(2), pages 1-23, February.
    4. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    5. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    6. Weide Li & Demeng Kong & Jinran Wu, 2017. "A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(5), pages 1-16, May.
    7. Shi, Yong & Ren, Xinyue & Guo, Kun & Zhou, Yi & Wang, Jun, 2020. "Research on the economic development pattern of Chinese counties based on electricity consumption," Energy Policy, Elsevier, vol. 147(C).
    8. Poonpong Suksawang & Sukonthip Suphachan & Kanokkarn Kaewnuch, 2018. "Electricity Consumption Forecasting in Thailand using Hybrid Model SARIMA and Gaussian Process with Combine Kernel Function Technique," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 98-109.
    9. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    10. Jinxin Liu & Guan Wang & Tong Zhao & Li Zhang, 2017. "Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine," Energies, MDPI, vol. 10(7), pages 1-14, July.
    11. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2018. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system," Renewable Energy, Elsevier, vol. 122(C), pages 533-550.
    12. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    13. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    14. Rodolfo Gordillo-Orquera & Luis Miguel Lopez-Ramos & Sergio Muñoz-Romero & Paz Iglesias-Casarrubios & Diego Arcos-Avilés & Antonio G. Marques & José Luis Rojo-Álvarez, 2018. "Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings," Energies, MDPI, vol. 11(3), pages 1-18, February.
    15. Mohammad Nasir & Ali Sadollah & Przemyslaw Grzegorzewski & Jin Hee Yoon & Zong Woo Geem, 2021. "Harmony Search Algorithm and Fuzzy Logic Theory: An Extensive Review from Theory to Applications," Mathematics, MDPI, vol. 9(21), pages 1-46, October.
    16. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    17. Li Wang & Jiguang Yue & Yongqing Su & Feng Lu & Qiang Sun, 2017. "A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression," Energies, MDPI, vol. 10(4), pages 1-22, April.
    18. Xueliang Li & Bingkang Li & Long Zhao & Huiru Zhao & Wanlei Xue & Sen Guo, 2019. "Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    19. George P. Papaioannou & Christos Dikaiakos & Anargyros Dramountanis & Panagiotis G. Papaioannou, 2016. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoot," Energies, MDPI, vol. 9(8), pages 1-40, August.

  10. Li-Ling Peng & Guo-Feng Fan & Min-Liang Huang & Wei-Chiang Hong, 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting," Energies, MDPI, vol. 9(3), pages 1-20, March.

    Cited by:

    1. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    2. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    3. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    4. Xing Liu & Jinhua Du & Deliang Liang, 2016. "Analysis and Speed Ripple Mitigation of a Space Vector Pulse Width Modulation-Based Permanent Magnet Synchronous Motor with a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 9(11), pages 1-15, November.
    5. Witold Orzeszko, 2021. "Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting," Energies, MDPI, vol. 14(19), pages 1-16, September.
    6. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
    7. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    8. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
    9. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2018. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system," Renewable Energy, Elsevier, vol. 122(C), pages 533-550.
    10. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    11. Li Wang & Jiguang Yue & Yongqing Su & Feng Lu & Qiang Sun, 2017. "A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression," Energies, MDPI, vol. 10(4), pages 1-22, April.
    12. Ming-Wei Li & Jing Geng & Shumei Wang & Wei-Chiang Hong, 2017. "Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting," Energies, MDPI, vol. 10(12), pages 1-18, December.

  11. Guo-Feng Fan & Shan Qing & Hua Wang & Wei-Chiang Hong & Hong-Juan Li, 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-15, April.

    Cited by:

    1. Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
    2. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    3. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    4. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    5. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    6. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
    7. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    8. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    9. Niu, Dongxiao & Ji, Zhengsen & Li, Wanying & Xu, Xiaomin & Liu, Da, 2021. "Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization," Energy, Elsevier, vol. 234(C).
    10. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    11. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    12. Radu Saulescu & Mircea Neagoe & Codruta Jaliu & Olimpiu Munteanu, 2021. "A Comparative Performance Analysis of Counter-Rotating Dual-Rotor Wind Turbines with Speed-Adding Increasers," Energies, MDPI, vol. 14(9), pages 1-21, May.
    13. María Del Carmen Ruiz-Abellón & Antonio Gabaldón & Antonio Guillamón, 2018. "Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees," Energies, MDPI, vol. 11(8), pages 1-22, August.
    14. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.

  12. Dong, Yucheng & Hong, Wei-Chiang & Xu, Yinfeng & Yu, Shui, 2013. "Numerical scales generated individually for analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 229(3), pages 654-662.

    Cited by:

    1. Bice Cavallo & Alessio Ishizaka, 2023. "Evaluating scales for pairwise comparisons," Annals of Operations Research, Springer, vol. 325(2), pages 951-965, June.
    2. Yuji Sato & Kim Hua Tan, 2023. "Inconsistency indices in pairwise comparisons: an improvement of the Consistency Index," Annals of Operations Research, Springer, vol. 326(2), pages 809-830, July.
    3. Vijay Pereira & Umesh Bamel, 2023. "Charting the managerial and theoretical evolutionary path of AHP using thematic and systematic review: a decadal (2012–2021) study," Annals of Operations Research, Springer, vol. 326(2), pages 635-651, July.
    4. Xia, Meimei & Chen, Jian, 2015. "Multi-criteria group decision making based on bilateral agreements," European Journal of Operational Research, Elsevier, vol. 240(3), pages 756-764.
    5. Ho, William & Ma, Xin, 2018. "The state-of-the-art integrations and applications of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 267(2), pages 399-414.
    6. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.
    7. Zhu, Bin & Xu, Zeshui, 2014. "Analytic hierarchy process-hesitant group decision making," European Journal of Operational Research, Elsevier, vol. 239(3), pages 794-801.
    8. Li, Cong-Cong & Dong, Yucheng & Liang, Haiming & Pedrycz, Witold & Herrera, Francisco, 2022. "Data-driven method to learning personalized individual semantics to support linguistic multi-attribute decision making," Omega, Elsevier, vol. 111(C).

  13. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.

    Cited by:

    1. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    2. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," AQR Working Papers 201506, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
    6. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    7. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    8. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    9. 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.
    10. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    11. Liu, Xin & Zhang, Zijun & Song, Zhe, 2020. "A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    12. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    13. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    14. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
    15. Guowei Cai & Wenjin Wang & Junhai Lu, 2016. "A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction," Energies, MDPI, vol. 9(12), pages 1-19, November.
    16. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    17. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    18. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    19. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    20. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.

  14. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.

    Cited by:

    1. Ming Meng & Dongxiao Niu & Wei Sun, 2011. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction," Energies, MDPI, vol. 4(10), pages 1-13, September.
    2. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    3. Tai-Liang Chen & Ching-Hsue Cheng & Jing-Wei Liu, 2019. "A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1967-1987, November.
    4. Taosheng Wang & Hongyan Zuo & C. H. Wu & B. Hu, 2021. "Combined soft measurement on key indicator parameters of new competitive advantages for China's export," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    5. Bahodirhon Safarov & Hisham Mohammad Al-Smadi & Makhina Buzrukova & Bekzot Janzakov & Alexandru Ilieş & Vasile Grama & Dorina Camelia Ilieș & Katalin Csobán Vargáné & Lóránt Dénes Dávid, 2022. "Forecasting the Volume of Tourism Services in Uzbekistan," Sustainability, MDPI, vol. 14(13), pages 1-18, June.
    6. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    7. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    8. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    9. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    10. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    11. Mohanad S. Al-Musaylh & Ravinesh C. Deo & Yan Li, 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms," Energies, MDPI, vol. 13(9), pages 1-19, May.
    12. Moustris, K. & Kavadias, K.A. & Zafirakis, D. & Kaldellis, J.K., 2020. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data," Renewable Energy, Elsevier, vol. 147(P1), pages 100-109.
    13. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
    14. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    15. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    16. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    17. Han Liu & Ying Liu & Yonglian Wang & Changchun Pan, 2019. "Hot topics and emerging trends in tourism forecasting research: A scientometric review," Tourism Economics, , vol. 25(3), pages 448-468, May.
    18. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    19. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    20. Lingyu, Tang & Jun, Wang & Chunyu, Zhao, 2021. "Mode decomposition method integrating mode reconstruction, feature extraction, and ELM for tourist arrival forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    21. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.

  15. Shih-Yung Wei & Wei-Chiang Samuelson Hong & Kai Wang, 2011. "Firm Size Transmission Effect and Price-Volume Relationship Analysis During Financial Tsunami Periods," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 2(3), pages 59-78, July.

    Cited by:

    1. Shih-Yung Wei & Li-Wei Lin & Surong Yan & Lu-jie Zhu, 2019. "Empirical Analysis on Price-Volume Relation in the Stock Market of China," International Journal of Economics and Financial Issues, Econjournals, vol. 9(5), pages 94-103.

  16. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.

    Cited by:

    1. Ko, Chia-Nan & Lee, Cheng-Ming, 2013. "Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter," Energy, Elsevier, vol. 49(C), pages 413-422.
    2. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    3. Yunxuan Dong & Jianzhou Wang & Chen Wang & Zhenhai Guo, 2017. "Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting," Energies, MDPI, vol. 10(4), pages 1-27, April.
    4. Pavel V. Matrenin & Vadim Z. Manusov & Alexandra I. Khalyasmaa & Dmitry V. Antonenkov & Stanislav A. Eroshenko & Denis N. Butusov, 2020. "Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting," Mathematics, MDPI, vol. 8(12), pages 1-17, December.
    5. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    6. Bramer, L.M. & Rounds, J. & Burleyson, C.D. & Fortin, D. & Hathaway, J. & Rice, J. & Kraucunas, I., 2017. "Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days," Applied Energy, Elsevier, vol. 205(C), pages 1408-1418.
    7. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    8. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    9. Xuguang Han & Jingming Su & Yan Hong & Pingshun Gong & Danping Zhu, 2022. "Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    10. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    11. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    12. Weijie Zhou & Huihui Tao & Huimin Jiang, 2022. "Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    13. Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
    14. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
    15. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    16. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    17. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    18. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    19. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    20. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    21. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    22. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    23. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
    24. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    25. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    26. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    27. Niu Dongxiao & Ma Tiannan & Liu Bingyi, 2017. "Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm," Journal of Combinatorial Optimization, Springer, vol. 33(3), pages 1122-1143, April.
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    Cited by:

    1. Ko, Chia-Nan & Lee, Cheng-Ming, 2013. "Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter," Energy, Elsevier, vol. 49(C), pages 413-422.
    2. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    3. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    4. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    6. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Wang, Jianzhou & Jia, Ruiling & Zhao, Weigang & Wu, Jie & Dong, Yao, 2012. "Application of the largest Lyapunov exponent and non-linear fractal extrapolation algorithm to short-term load forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1277-1287.
    8. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    9. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
    10. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    11. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    12. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    13. Pascal da Costa & Wenhui Tian, 2015. "A Sectoral Prospective Analysis of CO2 Emissions in China, USA and France, 2010-2050," Working Papers hal-01026302, HAL.
    14. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    15. Niu Dongxiao & Ma Tiannan & Liu Bingyi, 2017. "Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm," Journal of Combinatorial Optimization, Springer, vol. 33(3), pages 1122-1143, April.
    16. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    17. Sun, Shaolong & Qiao, Han & Wei, Yunjie & Wang, Shouyang, 2017. "A new dynamic integrated approach for wind speed forecasting," Applied Energy, Elsevier, vol. 197(C), pages 151-162.
    18. Guo-Feng Fan & Shan Qing & Hua Wang & Wei-Chiang Hong & Hong-Juan Li, 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-15, April.
    19. R. J. Kuo & Y. S. Tseng & Zhen-Yao Chen, 2016. "Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1191-1207, December.
    20. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
    21. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    22. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    23. Afshar, K. & Bigdeli, N., 2011. "Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)," Energy, Elsevier, vol. 36(5), pages 2620-2627.
    24. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    25. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    26. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    27. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    28. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    29. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    30. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    31. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).

  18. Wei-Chiang Hong & Ping-Feng Pai, 2007. "Potential assessment of the support vector regression technique in rainfall forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 495-513, February.

    Cited by:

    1. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    2. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    3. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
    4. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
    5. Krishna Singh & Mahesh Pal & V. Singh, 2010. "Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2007-2019, August.
    6. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    7. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    8. Roohollah Noori & Hossien Sheikhian & Farhad Hooshyaripor & Ali Naghikhani & Jan Franklin Adamowski & Behzad Ghiasi, 2017. "Granular Computing for Prediction of Scour Below Spillways," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 313-326, January.

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