Attention Based Energy Demand Forecasting in Smart Grid Environments

Yunus Emre Işıkdemir; Fuat Akal

Makale detayları

Attention Based Energy Demand Forecasting in Smart Grid Environments

Yazarlar:

Yunus Emre Işıkdemir | Fuat Akal

Yayıncı:

Fırat University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.227-240

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330483

pISSN:

2822-2881

Anahtar kelimeler:

Özet:

 The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8 better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 13 of the training time other models took. We also improved performance by 42 in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.

 The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8 better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 13 of the training time other models took. We also improved performance by 42 in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.

 The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8 better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 13 of the training time other models took. We also improved performance by 42 in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.

 Akıllı şebeke, modern enerji peyzajının kritik bir unsuru olup, artan enerji taleplerini karşılamak için güvenilir, verimli ve sürdürülebilir bir yol sağlamaktadır. Bununla birlikte, akıllı şebeke teknolojisi tarafından üretilen büyük miktardaki veri, gelişmiş veri işleme ve analiz tekniklerinin geliştirilmesini gerektirmektedir. Bu makalede, akıllı şebeke uygulamalarında zaman serisi tahmininde kullanılmak üzere, dilatasyonlu konvolüsyon ve dikkat mekanizmalarını birleştiren bir dikkat tabanlı zaman serisi iş akışı öneriyoruz. Bu akış, dilatasyonlu konvolüsyonları kullanarak zaman serisi verilerinden zamansal özellikler çıkarır ve dikkat mekanizmalarını kullanarak gizli durumlardaki önemli zaman noktalarını vurgular. Deneysel değerlendirmeler sonucunda, enerji talebi tahmininde, yaygın olarak kullanılan derin öğrenme tabanlı yöntemlere göre 8e kadar daha iyi bir performans gösterdiği gözlemlendi. Bu kazancı diğer modellerin aldığı eğitim süresinin yalnızca 13ü kadar bir sürede elde edilmiştir. Ayrıca, tamamen farklı bir alanda 42lik bir kazanç elde edilmiştir ve akışın diğer alanlara uyarlanabileceği gösterilmiştir. Bu çalışma, araştırmacılara akıllı şebeke uygulamaları için daha doğru ve verimli tahmin modelleri geliştirmelerine yardımcı olabilir, ayrıca enerji sistemlerinin sürdürülebilir yönetimi ve akıllı şebeke operasyonlarının optimizasyonu için yapay zeka ve dikkat tabanlı tahmin tekniklerinin potansiyeli hakkında değerli bilgiler sunabilir.

Makale detayları

Attention Based Energy Demand Forecasting in Smart Grid Environments

Yazarlar:

Yunus Emre Işıkdemir | Fuat Akal

Yayıncı:

Fırat University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.227-240

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330483

pISSN:

2822-2881

Smart grid

Time series forecasting

Attention

Transformers

Energy demand forecastingAkıllı şebeke

Zaman serisi tahmini

Attention mekanizması

Dönüştürücüler

Enerji talebi tahmini

Sayı

Makale detayları

Yazarlar: Yunus Emre Işıkdemir | Fuat Akal

Yayıncı: Fırat University

Ana Başlıklar: Yazılım Mühendisliği (Diğer)

Dergi: Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.227-240

Belge Tipi: Makale

Yayın Tarihi: 10/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001330483

pISSN: 2822-2881

Sayı

Yazarlar:

Yunus Emre Işıkdemir | Fuat Akal

Yayıncı:

Fırat University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.227-240

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330483

pISSN:

2822-2881