Integral Equations Related to Volterra Series and Inverse Problems: Elements of Theory and Applications in Heat Power Engineering
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- A. S. Apartsyn & S. V. Solodusha & V. A. Spiryaev, 2013. "Modeling of Nonlinear Dynamic Systems with Volterra Polynomials: Elements of Theory and Applications," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 2(4), pages 16-43, October.
- 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.
- Zhou, Hong & Chen, Cheng & Lai, Jingang & Lu, Xiaoqing & Deng, Qijun & Gao, Xingran & Lei, Zhongcheng, 2018. "Affine nonlinear control for an ultra-supercritical coal fired once-through boiler-turbine unit," Energy, Elsevier, vol. 153(C), pages 638-649.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- Wu, Chunying & Sun, Lingfang & Piao, Heng & Yao, Lijia, 2024. "Adaptive fuzzy finite time integral sliding mode control of the coordinated system for 350 MW supercritical once-through boiler unit to enhance flexibility," Energy, Elsevier, vol. 302(C).
- Andrea Bonfiglio & Massimo Brignone & Marco Invernizzi & Alessandro Labella & Daniele Mestriner & Renato Procopio, 2017. "A Simplified Microgrid Model for the Validation of Islanded Control Logics," Energies, MDPI, vol. 10(8), pages 1-28, August.
- Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
- Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
- Yury Voscoboynikov & Svetlana Solodusha & Evgeniia Markova & Ekaterina Antipina & Vasilisa Boeva, 2022. "Identification of Quadratic Volterra Polynomials in the “Input–Output” Models of Nonlinear Systems," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
- Zhang, Guangming & Zhang, Chao & Wang, Wei & Cao, Huan & Chen, Zhenyu & Niu, Yuguang, 2023. "Offline reinforcement learning control for electricity and heat coordination in a supercritical CHP unit," Energy, Elsevier, vol. 266(C).
- Esmaeili, Mohammad & Moradi, Hamed, 2023. "Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem," Energy, Elsevier, vol. 282(C).
- Zhang, Hongfu & Gao, Mingming & Fan, Haohao & Zhang, Kaiping & Zhang, Jiahui, 2022. "A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units," Energy, Elsevier, vol. 241(C).
- Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
- Fan, He & Su, Zhi-gang & Wang, Pei-hong & Lee, Kwang Y., 2021. "A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units," Energy, Elsevier, vol. 226(C).
- Thomas Price & Gordon Parker & Gail Vaucher & Robert Jane & Morris Berman, 2022. "Microgrid Energy Management during High-Stress Operation," Energies, MDPI, vol. 15(18), pages 1-11, September.
- Wu, Zhenlong & Li, Donghai & Xue, Yali & Chen, YangQuan, 2019. "Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions," Energy, Elsevier, vol. 185(C), pages 744-762.
- Cheng-Ming Lee & Chia-Nan Ko, 2016. "Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network," Energies, MDPI, vol. 9(12), pages 1-15, November.
- Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
- Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
- Zhang, Guojie & Wang, Xiaogang & Wiśniewski, Piotr & Chen, Jiaheng & Qin, Xiang & Dykas, Sławomir, 2023. "Effect of NaCl presence caused by salting out on the heterogeneous-homogeneous coupling non-equilibrium condensation flow in a steam turbine cascade," Energy, Elsevier, vol. 263(PE).
- Li, Wei-Qin & Chang, Li, 2018. "A combination model with variable weight optimization for short-term electrical load forecasting," Energy, Elsevier, vol. 164(C), pages 575-593.
- Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.
- Huang, Congzhi & Sheng, Xinxin, 2020. "Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm," Energy, Elsevier, vol. 205(C).
More about this item
Keywords
integral equations; integro-algebraic equations; Volterra series; mathematical modeling;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1905-:d:611759. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.