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Short-Term Load Forecasting on Individual Consumers

Author

Listed:
  • João Victor Jales Melo

    (Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil)

  • George Rossany Soares Lira

    (Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil)

  • Edson Guedes Costa

    (Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil)

  • Antonio F. Leite Neto

    (Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil)

  • Iago B. Oliveira

    (Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil)

Abstract

Maintaining stability and control over the electric system requires increasing information about the consumers’ profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. Nonetheless, predicting the profile of an individual consumer is a difficult task. The main challenge lies in the uncertainty related to the individual consumption profile, which increases forecasting errors. Thus, this paper aims to implement a load predictive model focused on individual consumers taking into account its randomness. For this purpose, a methodology is proposed to determine and select predictive features for individual STLF. The load forecasting of an individual consumer is simulated based on the four main machine learning techniques used in the literature. A 2.73% reduction in the forecast error is obtained after the correct selection of the predictive features. Compared to the baseline model (persistent forecasting method), the error is reduced by up to 19.8%. Among the techniques analyzed, support vector regression (SVR) showed the smallest errors (8.88% and 9.31%).

Suggested Citation

  • João Victor Jales Melo & George Rossany Soares Lira & Edson Guedes Costa & Antonio F. Leite Neto & Iago B. Oliveira, 2022. "Short-Term Load Forecasting on Individual Consumers," Energies, MDPI, vol. 15(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5856-:d:886663
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    References listed on IDEAS

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    1. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    2. Jonathan Roth & Jayashree Chadalawada & Rishee K. Jain & Clayton Miller, 2021. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification," Energies, MDPI, vol. 14(5), pages 1-22, March.
    3. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
    4. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
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