An LSTM model for power grid loss prediction

Jarkko Tulensalo, Janne Seppänen*, Alexander Ilin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)
88 Downloads (Pure)


With the changes that renewable energy sources bring to the electricity markets in all over the world, prediction of grid losses gets more complex as current methods have limited capability to take local weather conditions into account. This paper suggests a Long Short-Term Memory (LSTM) recurrent neural network model for power grid loss prediction. The model learns long-term relations of hourly time series data from electricity markets, local weather and calendar. We apply the model to predict the total transmission grid losses in Finland. We find that the proposed model outperforms both the reference method currently used in industry and linear regression proposed by previous studies.

Original languageEnglish
Article number106823
Pages (from-to)1-4
Number of pages4
JournalElectric Power Systems Research
Publication statusPublished - Dec 2020
MoE publication typeA1 Journal article-refereed


  • Deep neural networks
  • Long short-term memory
  • Power grid loss prediction
  • Recurrent neural networks


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