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, the load shedding and the virtual power plants. 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 minutes for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different type of samples, which have been classified into 'holidays', 'load shedding' and 'maintenance' in the training dataset has been investigated by calculating the most commonly used error metrics showing the importance of data employed in load forecast.

Data quality analysis in day-ahead load forecast by means of LSTM

Nespoli A.;Ogliari E.;Pretto S.;
2020-01-01

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, the load shedding and the virtual power plants. 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 minutes for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different type of samples, which have been classified into 'holidays', 'load shedding' and 'maintenance' in the training dataset has been investigated by calculating the most commonly used error metrics showing the importance of data employed in load forecast.
Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
978-1-7281-7455-6
Load forecast
Load shedding
LSTM
virtual power plant
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1152491
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