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Electrical Load Forecast by Means of LSTM: The Impact of Data Quality

Author

Listed:
  • Alfredo Nespoli

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy)

  • Emanuele Ogliari

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy)

  • Silvia Pretto

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy)

  • Michele Gavazzeni

    (Tecnowatt S.r.l., via dell’Aeronautica, 18, 24035 Curno, Italy)

  • Sonia Vigani

    (Tecnowatt S.r.l., via dell’Aeronautica, 18, 24035 Curno, Italy)

  • Franco Paccanelli

    (Tecnowatt S.r.l., via dell’Aeronautica, 18, 24035 Curno, Italy)

Abstract

Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in depth. In this research paper, the dependency of the day-ahead load forecast accuracy on the basis of the data typology employed in the training of LSTM has been inspected. A real case study of an Italian industrial load with samples recorded every 15 min for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different dataset cleaning approaches was investigated. In addition, the Generalised Extreme Studentized Deviate hypothesis testing was introduced to identify the outliers present in the dataset. The populations were constructed on the basis of an autocorrelation analysis that allowed for identifying a weekly correlation of the samples. The accuracy of the prediction obtained from different input dataset has been therefore investigated by calculating the most commonly used error metrics, showing the importance of data processing before employing them for load forecast.

Suggested Citation

  • Alfredo Nespoli & Emanuele Ogliari & Silvia Pretto & Michele Gavazzeni & Sonia Vigani & Franco Paccanelli, 2021. "Electrical Load Forecast by Means of LSTM: The Impact of Data Quality," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:6-101:d:495915
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    References listed on IDEAS

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    Cited by:

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    3. Eric Cebekhulu & Adeiza James Onumanyi & Sherrin John Isaac, 2022. "Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids," Sustainability, MDPI, vol. 14(5), pages 1-26, February.
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