IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223018649.html
   My bibliography  Save this article

Energy processes prediction by a convolutional radial basis function network

Author

Listed:
  • Rubio, José de Jesús
  • Garcia, Donaldo
  • Sossa, Humberto
  • Garcia, Ivan
  • Zacarias, Alejandro
  • Mujica-Vargas, Dante

Abstract

If an approach based on the gradient steepest descent is utilized to adapt the parameters of a radial basis function network, then it requires dimensionality reduction of the input dataset for the complexity reduction and efficiency improvement, resulting in a more precise energy processes prediction. The convolution operation could provide one way to perform dimensionality reduction of the input dataset. In this research, the convolutional radial basis function network is utilized for the energy processes prediction. The advances are exposed as follows: (1) the convolutional radial basis function network containing a convolution part, a hidden part, and an output part is utilized for the energy processes prediction, (2) the convolution operation is utilized in the convolution part to perform dimensionality reduction of the input dataset, and to change the magnitude of the input dataset for the complexity reduction, (3) the gradient steepest descent is utilized to adapt the parameters in the hidden part and output part for the efficiency improvement. The convolutional radial basis function network is compared against the radial basis function network, the feedforward neural network, and the neuro fuzzy system for the hourly electrical power demand prediction and for the chiller prediction.

Suggested Citation

  • Rubio, José de Jesús & Garcia, Donaldo & Sossa, Humberto & Garcia, Ivan & Zacarias, Alejandro & Mujica-Vargas, Dante, 2023. "Energy processes prediction by a convolutional radial basis function network," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223018649
    DOI: 10.1016/j.energy.2023.128470
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223018649
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128470?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Qiannan & Jiang, Feng & Li, Chaoshun, 2023. "Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization," Energy, Elsevier, vol. 271(C).
    2. Junpeng Huang & Sixiang Ling & Xiyong Wu & Rui Deng, 2022. "GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility," Land, MDPI, vol. 11(3), pages 1-25, March.
    3. Alao, Moshood Akanni & Popoola, Olawale M. & Ayodele, Temitope Rapheal, 2021. "Selection of waste-to-energy technology for distributed generation using IDOCRIW-Weighted TOPSIS method: A case study of the City of Johannesburg, South Africa," Renewable Energy, Elsevier, vol. 178(C), pages 162-183.
    4. de Oliveira Nogueira, Tiago & Palacio, Gilderlânio Barbosa Alves & Braga, Fabrício Damasceno & Maia, Pedro Paulo Nunes & de Moura, Elineudo Pinho & de Andrade, Carla Freitas & Rocha, Paulo Alexandre C, 2022. "Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines," Energy, Elsevier, vol. 238(PC).
    5. Alao, M.A. & Ayodele, T.R. & Ogunjuyigbe, A.S.O. & Popoola, O.M., 2020. "Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of Lagos, Nigeria," Energy, Elsevier, vol. 201(C).
    6. Huang, Xiaoqiao & Liu, Jun & Xu, Shaozhen & Li, Chengli & Li, Qiong & Tai, Yonghang, 2023. "A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting," Energy, Elsevier, vol. 272(C).
    7. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Ma, Lei & Chen, Bolong, 2023. "A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results," Energy, Elsevier, vol. 271(C).
    Full references (including those not matched with items on IDEAS)

    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.
    1. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    2. Sandylove Afrane & Jeffrey Dankwa Ampah & Ephraim Bonah Agyekum & Prince Oppong Amoh & Abdulfatah Abdu Yusuf & Islam Md Rizwanul Fattah & Ebenezer Agbozo & Elmazeg Elgamli & Mokhtar Shouran & Guozhu M, 2022. "Integrated AHP-TOPSIS under a Fuzzy Environment for the Selection of Waste-To-Energy Technologies in Ghana: A Performance Analysis and Socio-Enviro-Economic Feasibility Study," IJERPH, MDPI, vol. 19(14), pages 1-31, July.
    3. Dorokhov, V.V. & Kuznetsov, G.V. & Vershinina, K.Yu. & Strizhak, P.A., 2021. "Relative energy efficiency indicators calculated for high-moisture waste-based fuel blends using multiple-criteria decision-making," Energy, Elsevier, vol. 234(C).
    4. Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).
    5. Nondy, J. & Gogoi, T.K., 2021. "Performance comparison of multi-objective evolutionary algorithms for exergetic and exergoenvironomic optimization of a benchmark combined heat and power system," Energy, Elsevier, vol. 233(C).
    6. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    7. Mirza, Adeel Feroz & Mansoor, Majad & Usman, Muhammad & Ling, Qiang, 2023. "A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model," Energy, Elsevier, vol. 283(C).
    8. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
    9. Dong, Yuanxing & Xing, Li & Li, Xiaofeng & Gao, Yanfang & Cao, Zhenzhu & Liu, Jinrong, 2022. "A membrane-less molten hydroxide direct carbon fuel cell with fuel continuously supplied at low temperatures: A modeling and experimental study," Applied Energy, Elsevier, vol. 324(C).
    10. Kaytez, Fazıl, 2022. "Evaluation of priority strategies for the expansion of installed wind power capacity in Turkey using a fuzzy analytic network process analysis," Renewable Energy, Elsevier, vol. 196(C), pages 1281-1293.
    11. Prajapati, Parth & Patel, Vivek & Raja, Bansi D. & Jouhara, Hussam, 2023. "Multi objective ecological optimization of an irreversible Stirling cryogenic refrigerator cycle," Energy, Elsevier, vol. 274(C).
    12. Gezen, Mesliha & Karaaslan, Abdulkerim, 2022. "Energy planning based on Vision-2023 of Turkey with a goal programming under fuzzy multi-objectives," Energy, Elsevier, vol. 261(PA).
    13. Donald Ukpanyang & Julio Terrados-Cepeda & Manuel Jesus Hermoso-Orzaez, 2022. "Multi-Criteria Selection of Waste-to-Energy Technologies for Slum/Informal Settlements Using the PROMETHEE Technique: A Case Study of the Greater Karu Urban Area in Nigeria," Energies, MDPI, vol. 15(10), pages 1-26, May.
    14. Surbhi Upadhyay & Suresh Kumar Garg & Rishu Sharma, 2023. "Analyzing the Factors for Implementing Make-to-Order Manufacturing System," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    15. Chibuzo Nwabufo Okwuosa & Jang-wook Hur, 2022. "A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions," Energies, MDPI, vol. 15(20), pages 1-24, October.
    16. Aleksandra Bączkiewicz & Bartłomiej Kizielewicz & Andrii Shekhovtsov & Mykhailo Yelmikheiev & Volodymyr Kozlov & Wojciech Sałabun, 2021. "Comparative Analysis of Solar Panels with Determination of Local Significance Levels of Criteria Using the MCDM Methods Resistant to the Rank Reversal Phenomenon," Energies, MDPI, vol. 14(18), pages 1-21, September.
    17. Nondy, J. & Gogoi, T.K., 2022. "Tri-objective optimization of two recuperative gas turbine-based CCHP systems and 4E analyses at optimal conditions," Applied Energy, Elsevier, vol. 323(C).
    18. Dan Cudjoe, 2023. "Energy-economics and environmental prospects of integrated waste-to-energy projects in the Beijing-Tianjin-Hebei region," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12597-12628, November.
    19. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).
    20. Dmitrii Antonov & Olga Gaidukova & Galina Nyashina & Dmitrii Razumov & Pavel Strizhak, 2022. "Prospects of Using Gas Hydrates in Power Plants," Energies, MDPI, vol. 15(12), pages 1-20, June.

    Corrections

    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:eee:energy:v:284:y:2023:i:c:s0360544223018649. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.