IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipcs0360544222025270.html
   My bibliography  Save this article

Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem

Author

Listed:
  • Kottath, Rahul
  • Singh, Priyanka

Abstract

Swarm-based algorithms are widely accepted in different fields of engineering as they have proven themselves effective in solving real-world optimization problems. According to the “No-Free-Lunch” theorem, there is no such algorithm that can perform well in all optimization problems. This paper proposes a novel swarm-based optimization algorithm, influencer buddy optimization (IBO), that mimics human behavior in social environments. This algorithm exploits the fact that humans are influenced by a group of individuals rather than a single person. However, every individual can select a buddy from this group based on their preferences. The mathematical representation of this social human behavior is shown in this paper. The algorithm is tested on twenty-one standard benchmark functions, and twenty-four noiseless black-box optimization benchmarking (BBOB) functions to validate its effectiveness over fourteen optimization algorithms. Further, two hybrid models are proposed by combining IBO with an artificial neural network (ANN) and recurrent neural network (RNN) to solve electricity load and price forecasting problems. The results of hybrid models are compared with nine recent and popular optimization algorithms. The simulation result of hybrid models shows that ANN-IBO and RNN-IBO generate the least forecasting error in their respective classes of network architectures.

Suggested Citation

  • Kottath, Rahul & Singh, Priyanka, 2023. "Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222025270
    DOI: 10.1016/j.energy.2022.125641
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222025270
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125641?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. Ghadimi, Noradin & Akbarimajd, Adel & Shayeghi, Hossein & Abedinia, Oveis, 2018. "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting," Energy, Elsevier, vol. 161(C), pages 130-142.
    2. Singh, Priyanka & Dwivedi, Pragya, 2019. "A novel hybrid model based on neural network and multi-objective optimization for effective load forecast," Energy, Elsevier, vol. 182(C), pages 606-622.
    3. Karavezyris, Vassilios & Timpe, Klaus-Peter & Marzi, Ruth, 2002. "Application of system dynamics and fuzzy logic to forecasting of municipal solid waste," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 149-158.
    4. Hu-Sheng Wu & Feng-Ming Zhang, 2014. "Wolf Pack Algorithm for Unconstrained Global Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-17, March.
    5. Rainer Göb & Kristina Lurz & Antonio Pievatolo, 2013. "Electrical load forecasting by exponential smoothing with covariates," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(6), pages 629-645, November.
    6. Nguyen, Thang Trung, 2019. "A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization," Energy, Elsevier, vol. 171(C), pages 218-240.
    7. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    8. Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
    9. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    10. 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.
    11. Yang, Qiangda & Liu, Peng & Zhang, Jie & Dong, Ning, 2022. "Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation," Applied Energy, Elsevier, vol. 307(C).
    12. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Wu, Han & Liang, Yan & Gao, Xiao-Zhi & Du, Pei, 2024. "Auditory-circuit-motivated deep network with application to short-term electricity price forecasting," Energy, Elsevier, vol. 288(C).
    2. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).

    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. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    2. 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).
    3. 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.
    4. Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao & Wenqing Zhao, 2022. "Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    5. Singh, Priyanka & Kottath, Rahul, 2022. "Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices," Utilities Policy, Elsevier, vol. 79(C).
    6. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.
    7. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    8. Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
    9. Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao, 2022. "Short-Term Load Forecasting with an Ensemble Model Using Densely Residual Block and Bi-LSTM Based on the Attention Mechanism," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    10. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    11. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    13. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    14. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
    15. Sun, Yougang & Xu, Junqi & Lin, Guobin & Ni, Fei & Simoes, Rolando, 2018. "An optimal performance based new multi-objective model for heat and power hub in large scale users," Energy, Elsevier, vol. 161(C), pages 1234-1249.
    16. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    17. Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    18. Li, Ling-Ling & Ji, Bing-Xiang & Liu, Guan-Chen & Yuan, Jian-Ping & Tseng, Shuan-Wei & Lim, Ming K. & Tseng, Ming-Lang, 2024. "Grid-connected multi-microgrid system operational scheduling optimization: A hierarchical improved marine predators algorithm," Energy, Elsevier, vol. 294(C).
    19. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    20. Yang, Yang & Xue, Dingyü, 2016. "Continuous fractional-order grey model and electricity prediction research based on the observation error feedback," Energy, Elsevier, vol. 115(P1), pages 722-733.

    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:263:y:2023:i:pc:s0360544222025270. 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.