Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models

Sütçü M., Gülbahar I. T., Şahin K. N., Koloğlu Y., Çelikel M. E.

Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol.26, no.1, pp.91-104, 2022 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.16984/saufenbilder.982639
  • Journal Name: Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
  • Journal Indexes: Academic Search Premier, Business Source Elite, Business Source Premier, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.91-104
  • Abdullah Gül University Affiliated: Yes

Abstract

Load forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts.Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other.