Forecasting Electricity Consumption Using Deep Learning Methods with Hyperparameter Tuning


Ayvaz S., Arslan O.

28th Signal Processing and Communications Applications Conference (SIU), ELECTR NETWORK, 5 - 07 October 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu49456.2020.9302338
  • Country: ELECTR NETWORK
  • Keywords: Electricity consumption, Time series, Deep learning, Hyperparameter tuning, MODEL
  • Yıldız Technical University Affiliated: No

Abstract

In this study, it is tried to estimate one-day electricity consumption by using deep learning methods with a dataset which includes the change in time-dependent electricity consumption. After explaining the time series components and machine learning concepts, general information about previous studies on electricity consumption estimation is given. Since the dataset used is a time series, all the features are emphasized in detail and necessary operations like resample and differencing are performed before proceeding to the modeling. Tuning was applied on hyperparameters which significantly affect the performance of the algorithms used in the modeling stage and the most suitable parameters were searched for each method. Then the best results were compared with each other and the method with the lowest error rate was determined.