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Application of Metaheuristic Algorithms with Supervised Machine Learning for Accurate Power Consumption Prediction

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Abstract

Accurate power consumption prediction is a crucial part of energy management. Some of the machine learning models that are the focus of this study for the prediction of power use include Support Vector Regression, Adaptive Boosting, and Decision Tree Regression. These models have been improved with the use of some novel optimizers-namely, the Trochoid Search Optimization, Red-Tailed Hawk, and Giant Armadillo Optimization methods-for hyper-parameter tuning to enhance prediction accuracy. When tested against real data, DTGA outperformed with R2 values of 0.9918, 0.9924, and 0.9934 for three zones. This work extends the study on the forecast of power consumption by integrating machine learning and optimization techniques that provide effective energy management strategies.

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No datasets were generated or analysed during the current study.

References

  1. van Ruijven BJ, De Cian E, Sue Wing I. Amplification of future energy demand growth due to climate change. Nat Commun. 2019;10(1):2762. https://doi.org/10.1038/s41467-019-10399-3.

  2. Bhat JA. Renewable and non-renewable energy consumption—impact on economic growth and CO2 emissions in five emerging market economies. Environ Sci Pollut Res. 2018;25(35):35515–30.

    Article  Google Scholar 

  3. Yun S, Zhang Y, Xu Q, Liu J, Qin Y. Recent advance in new-generation integrated devices for energy harvesting and storage. Nano Energy. 2019;60:600–19.

    Article  MATH  Google Scholar 

  4. Zhang Z, Hong W-C, Li J. Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access. 2020;8:14642–58.

    Article  Google Scholar 

  5. Zorpas AA. Strategy development in the framework of waste management. Sci Total Environ. 2020;716:137088.

    Article  Google Scholar 

  6. Olu-Ajayi R, Alaka H, Sulaimon I, Sunmola F, Ajayi S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J Build Eng. 2022;45:103406.

    Article  Google Scholar 

  7. Amasyali K, El-Gohary N. Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renew Sustain Energy Rev. 2021;142:110714.

    Article  Google Scholar 

  8. Dong Z, Liu J, Liu B, Li K, Li X. Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification. Energy Build. 2021;241:110929.

    Article  MATH  Google Scholar 

  9. Amasyali K, El-Gohary NM. A review of data-driven building energy consumption prediction studies. Renew Sustain Energy Rev. 2018;81:1192–205.

    Article  MATH  Google Scholar 

  10. Li C, Ding Z, Zhao D, Yi J, Zhang G. Building energy consumption prediction: An extreme deep learning approach. Energies (Basel). 2017;10(10):1525.

    Article  MATH  Google Scholar 

  11. Zhao H, Magoulès F. A review on the prediction of building energy consumption. Renew Sustain Energy Rev. 2012;16(6):3586–92.

    Article  MATH  Google Scholar 

  12. Ali S, Bhargava A, Saxena A, Kumar P. A hybrid marine predator sine cosine algorithm for parameter selection of hybrid active power filter. Mathematics. 2023;11(3):598.

    Article  MATH  Google Scholar 

  13. Saxena A. A nonlinear hyperbolic optimized grey model for market clearing price prediction: Analysis and case study. Sustain Energy Grids Netw. 2024;38:101367.

    Article  MATH  Google Scholar 

  14. Madrid EA, Antonio N. Short-term electricity load forecasting with machine learning. Information. 2021;12(2):50.

  15. Habbak H, Mahmoud M, Metwally K, Fouda MM, Ibrahem MI. Load forecasting techniques and their applications in smart grids. Energies (Basel). 2023;16(3):1480.

    Article  Google Scholar 

  16. Zhao Y, Zhang C, Zhang Y, Wang Z, Li J. A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis. Energy Built Environ. 2020;1(2):149–64.

    Article  MATH  Google Scholar 

  17. Wang P, Liu B, Hong T. Electric load forecasting with recency effect: a big data approach. Int J Forecast. 2016;32(3):585–97.

    Article  MATH  Google Scholar 

  18. Mounir N, Ouadi H, Jrhilifa I. Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system. Energy Build. 2023;288:113022. https://doi.org/10.1016/j.enbuild.2023.113022.

    Article  Google Scholar 

  19. Tarmanini C, Sarma N, Gezegin C, Ozgonenel O. Short term load forecasting based on ARIMA and ANN approaches. Energy Rep. 2023;9:550–7. https://doi.org/10.1016/j.egyr.2023.01.060.

    Article  Google Scholar 

  20. Yang Y, et al. The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks. Energy. 2023;268:126548. https://doi.org/10.1016/j.energy.2022.126548.

    Article  MATH  Google Scholar 

  21. Malakouti SM. Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in Predicting Wind Speed and Energy Generation. Intell Syst Appl. 2023;19:200248.

    Google Scholar 

  22. Malakouti SM, Menhaj MB, Suratgar AA. The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction. Clean Eng Technol. 2023;15:100664.

    Article  MATH  Google Scholar 

  23. Malakouti SM. Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation. Case Stud Chem Environ Eng. 2023;8:100351.

    Article  MATH  Google Scholar 

  24. Vapnik VN. The nature of statistical learning theory. New York, USA: Springer-Verlag; 1995. ISBN: 978-1-4757-3264-1. Edition No.: 2. https://doi.org/10.1007/978-1-4757-3264-1

  25. Kiani J, Camp C, Pezeshk S. On the application of machine learning techniques to derive seismic fragility curves. Comput Struct. 2019;218:108–22. https://doi.org/10.1016/j.compstruc.2019.03.004.

    Article  MATH  Google Scholar 

  26. Freund Y. Boosting a weak learning algorithm by majority. Inf Comput. 1995;121(2):256–85.

    Article  MathSciNet  MATH  Google Scholar 

  27. Efraimidis PS, Spirakis PG. Weighted random sampling with a reservoir. Inf Process Lett. 2006;97(5):181–5.

    Article  MathSciNet  MATH  Google Scholar 

  28. Ferahtia S, et al. Red-tailed hawk algorithm for numerical optimization and real-world problems. Sci Rep. 2023;13(1):12950.

    Article  Google Scholar 

  29. Qais MH, Hasanien HM, Alghuwainem S. Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell. 2020;50:3926–41.

    Article  MATH  Google Scholar 

  30. Richter A, Boudinot BE, Garcia FH, Billen J, Economo EP, Beutel RG. Wonderfully weird: the head anatomy of the armadillo ant, Tatuidris tatusia (Hymenoptera: Formicidae: Agroecomyrmecinae), with evolutionary implications. Myrmecol News. 2023;33:35–53. https://doi.org/10.25849/myrmecol.news_033:035.

  31. Alsayyed O, et al. Giant armadillo optimization: a new bio-inspired metaheuristic algorithm for solving optimization problems. Biomimetics. 2023;8(8):619.

    Article  MATH  Google Scholar 

  32. Wang S, Wang S, Chen H, Gu Q. Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics. Energy. 2020;195:116964.

    Article  MATH  Google Scholar 

  33. Zhang P, Ma X, She K. A novel power-driven grey model with whale optimization algorithm and its application in forecasting the residential energy consumption in China. Complexity. 2019;2019(1):1510257.

    Article  MATH  Google Scholar 

  34. Shapi MKM, Ramli NA, Awalin LJ. Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. Dev Built Environ. 2021;5:100037.

    Article  Google Scholar 

  35. Fumo N, Biswas MAR. Regression analysis for prediction of residential energy consumption. Renew Sustain Energy Rev. 2015;47:332–43.

    Article  MATH  Google Scholar 

  36. Salam A, El Hibaoui A. omparison of machine learning algorithms for the power consumption prediction:-case study of tetouan city–. In 2018 6th International Renewable and Sustainable Energy Conference (IRSEC). IEEE. 2018;2018:1–5.

  37. Zeng A, Ho H, Yu Y. Prediction of building electricity usage using Gaussian Process Regression. J Build Eng. 2020;28:101054.

  38. Pham A-D, Ngo N-T, Truong TTH, Huynh N-T, Truong N-S. Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. J Clean Prod. 2020;260:121082.

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Social Innovation and Public Culture at Communication University of China, Beijing, 100000, China

    Chaoyang Zhu

  2. International Engineering Psychology Institute in the United States, Denver, CO, 80201, USA

    Mengxia Wang, Chaoyang Zhu, Jinxin Deng & Yiwei Cai

  3. University of Illinois at Champaign, Champaign, IL, 6180, USA

    Mengxia Wang & Chaoyang Zhu

  4. Hainan Vocational University of Science and Technology, Haikou, 570100, China

    Yunxiang Zhang

  5. Shenzhen High-level Talents Development Promotion Association, Shenzhen, 518000, China

    Mengxia Wang & Yunxiang Zhang

  6. CDA International Accelerator, Shenzhen, 518000, China

    Yunxiang Zhang

  7. Shenzhen Research Institute, Beijing Institute of Technology, Shenzhen, 518000, China

    Mengxia Wang

  8. University of Wollongong, Wollongong City, 2223, Australia

    Mengxia Wang

  9. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wei Wei

  10. Shandong Open University, Jinan, 250000, China

    Mengxing Guo

Authors
  1. Mengxia Wang
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Contributions

MENGXIA WANG: Formal analysis, Validation, Supervision, Language review. CHAOYANG ZHU: Methodology, Software, Validation, Formal analysis. YUNXIANG ZHANG: Writing-Original draft preparation, Conceptualization, Supervision, Project administration. JINXIN DENG: Methodology, Software, Validation, Formal analysis. YIWEI CAI: Formal analysis, Validation, Supervision, Language review. WEI WEI: Methodology, Software, Validation, Formal analysis. MENGXING GUO: Formal analysis, Validation, Supervision, Language review.

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Correspondence to Yunxiang Zhang.

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Wang, M., Zhu, C., Zhang, Y. et al. Application of Metaheuristic Algorithms with Supervised Machine Learning for Accurate Power Consumption Prediction. Cogn Comput 17, 59 (2025). https://doi.org/10.1007/s12559-025-10402-8

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