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Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Author

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  • Andrei M. Tudose

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Irina I. Picioroaga

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Dorian O. Sidea

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Constantin Bulac

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Valentin A. Boicea

    (Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

Abstract

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

Suggested Citation

  • Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4046-:d:588708
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    References listed on IDEAS

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    Cited by:

    1. Gianfranco Chicco & Andrea Mazza & Salvatore Musumeci & Enrico Pons & Angela Russo, 2022. "Editorial for the Special Issue “Verifying the Targets—Selected Papers from the 55th International Universities Power Engineering Conference (UPEC 2020)”," Energies, MDPI, vol. 15(15), pages 1-8, August.
    2. Neilson Luniere Vilaça & Marly Guimarães Fernandes Costa & Cicero Ferreira Fernandes Costa Filho, 2023. "A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm," Energies, MDPI, vol. 16(8), pages 1-14, April.
    3. S. M. Mahfuz Alam & Ahmed Abuhussein & Mohammad Ashraf Hossain Sadi, 2023. "Month-Wise Investigation on Residential Load Consumption Impact during COVID-19 Period on Distribution Transformer and Practical Mitigation Solution," Energies, MDPI, vol. 16(5), pages 1-24, February.
    4. Cristina Hora & Florin Ciprian Dan & Gabriel Bendea & Calin Secui, 2022. "Residential Short-Term Load Forecasting during Atypical Consumption Behavior," Energies, MDPI, vol. 15(1), pages 1-15, January.
    5. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
    6. Iuri C. Figueiró & Alzenira R. Abaide & Nelson K. Neto & Leonardo N. F. Silva & Laura L. C. Santos, 2023. "Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region," Energies, MDPI, vol. 16(19), pages 1-21, September.
    7. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.
    8. Sajawal ur Rehman Khan & Israa Adil Hayder & Muhammad Asif Habib & Mudassar Ahmad & Syed Muhammad Mohsin & Farrukh Aslam Khan & Kainat Mustafa, 2022. "Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids," Energies, MDPI, vol. 16(1), pages 1-16, December.

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