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Short-term load forecasting using a kernel-based support vector regression combination model

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  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. 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.
  3. Zhineng Hu & Jing Ma & Liangwei Yang & Xiaoping Li & Meng Pang, 2019. "Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand," Sustainability, MDPI, vol. 11(5), pages 1-25, February.
  4. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
  5. 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.
  6. Fermín Rodríguez & Fernando Martín & Luis Fontán & Ainhoa Galarza, 2020. "Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control," Energies, MDPI, vol. 13(19), pages 1-19, October.
  7. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
  8. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  9. Wang, Hu & Mao, Lei & Zhang, Heng & Wu, Qiang, 2024. "Multi-prediction of electric load and photovoltaic solar power in grid-connected photovoltaic system using state transition method," Applied Energy, Elsevier, vol. 353(PB).
  10. Yundong Gu & Dongfen Ma & Jiawei Cui & Zhenhua Li & Yaqi Chen, 2022. "Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory," Annals of Data Science, Springer, vol. 9(3), pages 485-501, June.
  11. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
  12. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a combined model based on multi-objective optimization for electrical load forecasting," Energy, Elsevier, vol. 119(C), pages 1057-1074.
  13. Heng, Jiani & Wang, Jianzhou & Xiao, Liye & Lu, Haiyan, 2017. "Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting," Applied Energy, Elsevier, vol. 208(C), pages 845-866.
  14. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
  15. Wang, Yongli & Wang, Huan & Meng, Xiao & Dong, Huanran & Chen, Xin & Xiang, Hao & Xing, Juntai, 2023. "Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast," Energy, Elsevier, vol. 285(C).
  16. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
  17. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
  18. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
  19. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
  20. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
  21. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
  22. He, Yaoyao & Zheng, Yaya, 2018. "Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function," Energy, Elsevier, vol. 154(C), pages 143-156.
  23. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
  24. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
  25. Jamali Jahromi, Ali & Mohammadi, Mohammad & Afrasiabi, Shahabodin & Afrasiabi, Mousa & Aghaei, Jamshid, 2022. "Probability density function forecasting of residential electric vehicles charging profile," Applied Energy, Elsevier, vol. 323(C).
  26. Jungwon Yu & June Ho Park & Sungshin Kim, 2018. "A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting," Energies, MDPI, vol. 11(11), pages 1-20, October.
  27. 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.
  28. Wu, Zhuochun & Zhao, Xiaochen & Ma, Yuqing & Zhao, Xinyan, 2019. "A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting," Applied Energy, Elsevier, vol. 237(C), pages 896-909.
  29. Jiaming Zhu & Peng Wu & Huayou Chen & Ligang Zhou & Zhifu Tao, 2018. "A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model," IJERPH, MDPI, vol. 15(9), pages 1-19, September.
  30. Yang, Youlong & Che, Jinxing & Deng, Chengzhi & Li, Li, 2019. "Sequential grid approach based support vector regression for short-term electric load forecasting," Applied Energy, Elsevier, vol. 238(C), pages 1010-1021.
  31. 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.
  32. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
  33. 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.
  34. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
  35. He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
  36. Yiqi Lu & Yongpan Li & Da Xie & Enwei Wei & Xianlu Bao & Huafeng Chen & Xiancheng Zhong, 2018. "The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load," Energies, MDPI, vol. 11(11), pages 1-16, November.
  37. Wang, Shaomin & Wang, Shouxiang & Chen, Haiwen & Gu, Qiang, 2020. "Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics," Energy, Elsevier, vol. 195(C).
  38. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
  39. Hongbin Xu & Qiang Peng & Yuhao Wang & Zengwen Zhan, 2023. "Power-Load Forecasting Model Based on Informer and Its Application," Energies, MDPI, vol. 16(7), pages 1-14, March.
  40. Fu, Xin & Zeng, Xiao-Jun & Feng, Pengpeng & Cai, Xiuwen, 2018. "Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China," Energy, Elsevier, vol. 165(PB), pages 76-89.
  41. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
  42. 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.
  43. Hu, Yi & Qu, Boyang & Wang, Jie & Liang, Jing & Wang, Yanli & Yu, Kunjie & Li, Yaxin & Qiao, Kangjia, 2021. "Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning," Applied Energy, Elsevier, vol. 285(C).
  44. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P. & Bouzerdoum, A., 2017. "Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment," Applied Energy, Elsevier, vol. 205(C), pages 790-801.
  45. Yang, YouLong & Che, JinXing & Li, YanYing & Zhao, YanJun & Zhu, SuLing, 2016. "An incremental electric load forecasting model based on support vector regression," Energy, Elsevier, vol. 113(C), pages 796-808.
  46. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
  47. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
  48. Lin Lin & Lin Xue & Zhiqiang Hu & Nantian Huang, 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours," Energies, MDPI, vol. 11(7), pages 1-30, July.
  49. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
  50. Siddik Shakul Hameed & Ramesh Ramadoss & Kannadasan Raju & GM Shafiullah, 2022. "A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression," Sustainability, MDPI, vol. 14(7), pages 1-29, April.
  51. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
  52. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
  53. Jiang, He & Tao, Changqi & Dong, Yao & Xiong, Ren, 2021. "Robust low-rank multiple kernel learning with compound regularization," European Journal of Operational Research, Elsevier, vol. 295(2), pages 634-647.
  54. Qi Jiang & Yuxin Cheng & Haozhe Le & Chunquan Li & Peter X. Liu, 2022. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
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