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

Alfredo Nespoli, Emanuele Ogliari, Silvia Pretto, Michele Gavazzeni, Sonia Vigani and Franco Paccanelli
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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

Forecasting, 2021, vol. 3, issue 1, 1-11

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.

Keywords: load forecast; outliers detection; LSTM; machine learning (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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