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Electrical Load Forecast by Means of LSTM: The Impact of Data Quality
- Source :
- Forecasting, Vol 3, Iss 6, Pp 91-102 (2021), Forecasting, Volume 3, Issue 1, Pages 6-101
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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.
- Subjects :
- Data processing
Studentized range
Electrical load
Computer science
lcsh:Mathematics
020209 energy
Autocorrelation
Aggregate (data warehouse)
02 engineering and technology
lcsh:QA1-939
computer.software_genre
load forecast
machine learning
Data quality
Outlier
outliers detection
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Data mining
LSTM
lcsh:Science (General)
computer
lcsh:Q1-390
Statistical hypothesis testing
Subjects
Details
- ISSN :
- 25719394
- Volume :
- 3
- Database :
- OpenAIRE
- Journal :
- Forecasting
- Accession number :
- edsair.doi.dedup.....b8255e4646cad8a9b21e095b51992270