Electric demand forecasting with neural networks and symbolic time series representations

This paper addresses the electric demand prediction problem using neural networks and symbolization techniques. Symbolization techniques provide a time series symbolic representation of a lower length than the original time series. In our methodology, we incorporate the use of encoding from ordinal regression, preserving the notation of order between the symbols and make extensive experimentation with different neural network architectures and symbolization techniques. In our experimentation, we used the total electric demand data in the Spanish peninsula electric network, taken from 2009 to 2019 with a granularity of 10 min. The best model found making use of the symbolization methodology offered us slightly worse quality metrics (1.3655 RMSE and 0.0390 MAPE instead of the 1.2889 RMSE and 0.0363 MAPE from the best numerical model) but it was trained 6826 times faster. (C) 2022 Elsevier B.V. All reserved.

Automated summary

This paper addresses the problem of electric demand prediction using neural networks and symbolization techniques. Symbolization techniques provide a shorter symbolic representation of a time series compared to the original time series. In the experimentation, the symbolization methodology resulted in a model that was trained significantly faster but had slightly worse quality metrics compared to the best numerical model.