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Journals / Sakarya University Journal of Science / 2022 / Cilt: 26 Sayı: 1

Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models

Muhammed SÜTÇÜ
Kübra Nur ŞAHİN
Yunus KOLOĞLU
Mevlüt Emirhan ÇELİKEL
İbrahim Tümay GÜLBAHAR
Pages
91–104
Publication Date
2022-02-28
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Load forecasting is an essential task which is executed by electricity retail companies.
📊
Key Findings & Data:By predicting the demand accurately, companies can prevent waste of resources and blackouts.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Load forecasting · deep learning · regression by classification

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.

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Cite This Article

SÜTÇÜ, M., ŞAHİN, K. N., KOLOĞLU, Y., ÇELİKEL, M. E., GÜLBAHAR, İ. T. (2022). Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models. Sakarya University Journal of Science, Cilt: 26 Sayı: 1, 91–104. https://ajindex.com/articles/941722

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