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Net load forecasting for high renewable energy penetration grids

Citations

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Cited by:

  1. Sreekumar, S. & Khan, N.U. & Rana, A.S. & Sajjadi, M. & Kothari, D.P., 2022. "Aggregated Net-load Forecasting using Markov-Chain Monte-Carlo Regression and C-vine copula," Applied Energy, Elsevier, vol. 328(C).
  2. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
  3. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
  4. 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).
  5. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
  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. Heymann, Fabian & Miranda, Vladimiro & Soares, Filipe Joel & Duenas, Pablo & Perez Arriaga, Ignacio & Prata, Ricardo, 2019. "Orchestrating incentive designs to reduce adverse system-level effects of large-scale EV/PV adoption – The case of Portugal," Applied Energy, Elsevier, vol. 256(C).
  8. Nakamura, Kotaro & Muramatsu, Takehiko & Ogawa, Takashi & Nakagaki, Takao, 2021. "Prediction of de-NOx performance using monolithic SCR catalyst under load following operation of natural gas-fired combined cycle power plants," Energy, Elsevier, vol. 227(C).
  9. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
  10. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
  11. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
  12. Sreekumar, Sreenu & Yamujala, Sumanth & Sharma, Kailash Chand & Bhakar, Rohit & Simon, Sishaj P. & Rana, Ankur Singh, 2022. "Flexible Ramp Products: A solution to enhance power system flexibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
  13. Shi, Jiaqi & Liu, Nian & Huang, Yujing & Ma, Liya, 2022. "An Edge Computing-oriented Net Power Forecasting for PV-assisted Charging Station: Model Complexity and Forecasting Accuracy Trade-off," Applied Energy, Elsevier, vol. 310(C).
  14. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
  15. Zhao, Mingzhe & Wang, Yimin & Wang, Xuebin & Chang, Jianxia & Chen, Yunhua & Zhou, Yong & Guo, Aijun, 2022. "Flexibility evaluation of wind-PV-hydro multi-energy complementary base considering the compensation ability of cascade hydropower stations," Applied Energy, Elsevier, vol. 315(C).
  16. 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).
  17. Meng, Anbo & Li, Hanhong & Yin, Hao & Li, Xuecong, 2025. "A novel net load prediction approach using multi-scale deep learning network based on Trend-Multi-Period encoder and segment imbalance regression," Energy, Elsevier, vol. 332(C).
  18. Alyssa Diva Mustika & Rémy Rigo-Mariani & Vincent Debusschere & Amaury Pachurka, 2022. "New Members Selection for the Expansion of Energy Communities," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
  19. Li, Tianxiao & Li, Zheng & Li, Weiqi, 2020. "Scenarios analysis on the cross-region integrating of renewable power based on a long-period cost-optimization power planning model," Renewable Energy, Elsevier, vol. 156(C), pages 851-863.
  20. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
  21. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
  22. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  23. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
  24. Biyun Chen & Qi Xu & Zhuoli Zhao & Xiaoxuan Guo & Yongjun Zhang & Jingmin Chi & Canbing Li, 2023. "A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
  25. Rubasinghe, Osaka & Zhang, Tingze & Zhang, Xinan & Choi, San Shing & Chau, Tat Kei & Chow, Yau & Fernando, Tyrone & Iu, Herbert Ho-Ching, 2023. "Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration," Applied Energy, Elsevier, vol. 333(C).
  26. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
  27. Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
  28. Chi Hua & Erxi Zhu & Liang Kuang & Dechang Pi, 2019. "Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
  29. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  30. Liu, Tianhao & Li, Fangning & Zhang, Dongdong & Shan, Linke & Zhu, Hongyu & Du, Pengcheng & Jiang, Meihui & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Kong, Fannie, 2026. "Intelligent load forecasting technologies for diverse scenarios in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
  31. Pierro, Marco & De Felice, Matteo & Maggioni, Enrico & Moser, David & Perotto, Alessandro & Spada, Francesco & Cornaro, Cristina, 2020. "Residual load probabilistic forecast for reserve assessment: A real case study," Renewable Energy, Elsevier, vol. 149(C), pages 508-522.
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