IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v73y2014icp916-925.html
   My bibliography  Save this item

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Yin, He & Yang, Mao-sen & Lan, Hai & Hong, Ying-Yi & Guo, Dong & Jin, Feng, 2024. "A hybrid graph attention network based method for interval prediction of shipboard solar irradiation," Energy, Elsevier, vol. 298(C).
  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. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
  4. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
  5. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Sultan Al-Yahyai & Mostafa Bakhtvar, 2019. "An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems," Energies, MDPI, vol. 12(22), pages 1-20, November.
  6. Ma, Yixiang & Yu, Lean & Zhang, Guoxing & Lu, Zhiming & Wu, Jiaqian, 2023. "Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling," Renewable Energy, Elsevier, vol. 219(P1).
  7. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
  8. Xiaoyu Zhang & Zhe Shu & Rui Wang & Tao Zhang & Yabing Zha, 2018. "Short-Term Load Interval Prediction Using a Deep Belief Network," Energies, MDPI, vol. 11(10), pages 1-18, October.
  9. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
  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. 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.
  13. Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
  14. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
  15. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
  16. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
  17. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao, 2024. "Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model," Energy, Elsevier, vol. 300(C).
  18. 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.
  19. Fuqiang Li & Shiying Zhang & Wenxuan Li & Wei Zhao & Bingkang Li & Huiru Zhao, 2019. "Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
  20. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
  21. Vasileios Evangelopoulos & Panagiotis Karafotis & Pavlos Georgilakis, 2020. "Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method," Energies, MDPI, vol. 13(18), pages 1-25, September.
  22. Hiroki Yamamoto & Junji Kondoh & Daisuke Kodaira, 2022. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation," Energies, MDPI, vol. 15(15), pages 1-17, July.
  23. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
  24. Jiyang Wang & Yuyang Gao & Xuejun Chen, 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 11(6), pages 1-30, June.
  25. 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.
  26. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
  27. 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.
  28. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
  29. 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.
  30. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
  31. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
  32. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  33. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
  34. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.