Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
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. © 2025 Elsevier B.V., All rights reserved.
Description
Keywords
Deep Learning, Load Forecasting, Regression By Classification, Bilgisayar Bilimleri, Yazılım Mühendisliği, İstatistik Ve Olasılık, İktisat, Endüstri Mühendisliği, Load forecasting, load forecasting, deep learning, regression by classification, Engineering (General). Civil engineering (General), Load forecasting;deep learning;regression by classification, Chemistry, Industrial Engineering, TA1-2040, QD1-999
Fields of Science
0211 other engineering and technologies, 02 engineering and technology, 0202 electrical engineering, electronic engineering, information engineering
Citation
WoS Q
Scopus Q
OpenCitations Citation Count
4
Volume
26
Issue
1
Start Page
91
End Page
104