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Volume 17, Issue 10
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

Consumption of electricity always varies based on demand. The load cluster pattern aims at categorizing periodical changes over a specific time. Predicting the electric load was the initial goal of this study. Additionally, the outcomes of the load prediction were utilized as data for categorizing electrical loads using a descriptive-analytical method.

Objective

The study has dealt with a matching of load-side electric demand with the electric supply. To ensure dependable power-generating stability, it is vital to anticipate and categorize loads. Thus, the research presented here has focused on electrical load forecasting and classification.

Methods

Alternative algorithms, including Naive Bayes, decision tree, and support vector machine classifier, were employed to address the cluster pattern. The data used for this research presentation was collected from the D Block of the Kamaraj College of Engineering and Technology, K. Vellakulam, India, every 15 minutes. Multiple unsuitable loaded circumstances were ignored during the pre-processing of the dataset. Additionally, other algorithms, like Naive Bayes, decision tree, and support vector machine, were used to categorize the raw data. The processing of data was done by a feature selection approach.

Results

The performance was predicted by comparing the entire machine learning algorithms. Out of the machine learning techniques, an accuracy of 4.2% for Academic Block 4, a precision of 33% for Boys Hostel, a recall score of 4.7% for Academic Block 4, and an F1 score of 5.3% for Academic Block 4, were obtained.

Conclusion

In the study, the decision tree algorithm has shown promising performance than the other machine learning techniques used.

© 2024 Bentham Science Publishers
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2023-10-18
2025-06-25

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References

  1. PapaefthymiouG. HaesenE. SachT. Power system flexibility tracker: Indicators to track flexibility progress towards high-res systems.Renew. Energy20181271026103510.1016/j.renene.2018.04.094
     [Google Scholar]
  2. García-MartínE. RodriguesC.F. RileyG. GrahnH. Estimation of energy consumption in machine learning.J. Parallel Distrib. Comput.2019134758810.1016/j.jpdc.2019.07.007
     [Google Scholar]
  3. MosaviA. BahmaniA. Energy consumption prediction using machine learning: A reviewPreprints20190301312019
     [Google Scholar]
  4. CandanedoI.S. NievesE.H. GonzálezS.R. MartínM.T.S. BrionesA.G. Machine learning predictive model for industry 4.0. In Knowledge Management in Organizations 13th International Conference, KMO 2018, Žilina, Slovakia, Proceedings 13Springer International Publishing501510August 6–10, 2018
     [Google Scholar]
  5. González BrionesA. ChamosoP. BarriusoA. Review of the main security problems with multi-agent systems used in e-commerce applications.Adv. Dist. Comp. Artif. Intell. J.201653556110.14201/ADCAIJ2016535561
     [Google Scholar]
  6. HimeurY. GhanemK. AlsalemiA. BensaaliF. AmiraA. Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives.Appl. Energy202128711660110.1016/j.apenergy.2021.116601
     [Google Scholar]
  7. HajirahimiZ. KhasheiM. Hybridization of hybrid structures for time series forecasting: A review.Artif. Intell. Rev.20235621201126110.1007/s10462‑022‑10199‑0
     [Google Scholar]
  8. YottoH.C.S. ChetangnyP. ZogbochiV. AredjodounJ. HoundedakoS. BarbierG. VianouA. ChamagneD. "Long-term electricity load forecasting using artificial neural network: The case study of beninAdvanced Engineering Forum, Trans Tech Publications Ltd.Trans Tech Publications Ltd.481171362023
     [Google Scholar]
  9. AlhendiA. Al-SumaitiA.S. MarzbandM. KumarR. DiabA.A.Z. Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England.Energy Rep.202394799481510.1016/j.egyr.2023.03.116
     [Google Scholar]
  10. TarmaniniC. SarmaN. GezeginC. OzgonenelO. Short term load forecasting based on ARIMA and ANN approaches.Energy Rep.2023955055710.1016/j.egyr.2023.01.060
     [Google Scholar]
  11. LiL. GuoL. WangJ. PengH. Short-term load forecasting based on spiking neural p systems.Appl. Sci.202313279210.3390/app13020792
     [Google Scholar]
  12. HippertH.S. PedreiraC.E. SouzaR.C. Neural networks for short-term load forecasting: A review and evaluation.IEEE Trans. on Power Syst.2001161445510.1109/59.910780
     [Google Scholar]
  13. BurnettJ.W. KieslingL.L. How do machines predict energy use? Comparing machine learning approaches for modeling household energy demand in the United States.Energy Res. Soc. Sci.20229110271510.1016/j.erss.2022.102715
     [Google Scholar]
  14. Ming-WeiC. Bo-JuenC. Ming-WeiC. Chih-JenLin Load forecasting using support vector machines: A study on eunite competition 2001Power Systems, IEEE Transactions200419418211830
     [Google Scholar]
  15. MonacchiA. EgarterD. ElmenreichW. "Integrating households into the smart grid", Mode. Simul. Cyber.Phy. Ener. Sys.MSCPES201316
     [Google Scholar]
  16. ZahariaM. An Architecture for Fast and General Data Processing on Large Clusters.PhD thesis, EECS Department, University of California: Berkeley, 2014
     [Google Scholar]
  17. CepericE. CepericV. BaricA. A strategy for short-term load forecasting by support vector regression machines.IEEE Trans. on Power Syst.20132844356436410.1109/TPWRS.2013.2269803
     [Google Scholar]
  18. LiS. KongX. YueL. LiuC. KhanM.A. YangZ. ZhangH. Short-term electrical load forecasting using hybrid model of manta ray foraging optimization and support vector regression.J. Clean. Prod.202338813585610.1016/j.jclepro.2023.135856
     [Google Scholar]
  19. LangevinA. CherietM. GagnonG. Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring.Sustainable Energy.Grids and Networks202334101006
     [Google Scholar]
  20. LeeG.C. Regression-based methods for daily peak load forecasting in South Korea.Sustainability2022147398410.3390/su14073984
     [Google Scholar]
/content/journals/raeeng/10.2174/0123520965258879231011182850
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Energy Monitoring for Renewable Energy System Using Machine Learning Algorithms
Recent Advances in Electrical & Electronic Engineering 17, 966 (2024); https://doi.org/10.2174/0123520965258879231011182850
/content/journals/raeeng/10.2174/0123520965258879231011182850
/content/journals/raeeng/10.2174/0123520965258879231011182850
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  • Article Type:     Research Article
Keyword(s): algorithms; Energy monitoring system; load identification model; load prediction; machine learning techniques; supervised learning

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