Article detail · 2019
A Comparative Study of Machine Learning and Deep Learning for Time Series Forecasting: A Case Study of Choosing the Best Prediction Model for Turkey Electricity Production
Journal
Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü DergisiISSN 1308-6529
- Year
- 2019
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Over the last decades, Turkey pays special attention to electricity productionbto afford its needs. Researchers applied different methodologies including statisticalbased and artificial intelligence-based to correctly predict the future amount of electricity production, consumption, and demand. However,limited researchers focused on Turkey’s electricity production prediction problem as a time series analysis. For this reason, we tackle this problem by considering it as a time series analysis in this study. We have used different methods including traditional machine learning algorithms Support Vector Regression (SVR) and Multilayer Perceptrons (MLP) and a deep learning algorithm Long Short-Term Memory (LSTM) to create a better model for Turkey monthly electricity production dataset. Based on our findings LSTM outperforms SVR and MLP approaches in terms of commonly used statistical error evaluation metrics.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
13 citations
OpenAlex cited_by_count (cache / database)
13 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Artificial neural network approach for monthly air temperature estimations and maps 2023
- Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye 2023
- Application of Long Short-Term Memory (LSTM) Neural Network Based on Deep Learning for Electricity Energy Consumption Forecasting 2022
- Application of Long Short-Term Memory (LSTM) Neural Network Based on Deep Learning for Electricity Energy Consumption Forecasting 2022
- Application of Long Short-Term Memory (LSTM) Neural Network Based on Deep Learning for Electricity Energy Consumption Forecasting 2022
- Deep learning based electricity demand forecasting to minimize the cost of energy imbalance: A real case application with some fortune 500 companies in Türkiye 2023
- A reduced variance unsupervised ensemble learning algorithm based on modern portfolio theory 2021
- Makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak e-perakende sektörüne yönelik talep tahmini 2022
- Demand forecasting for e-retail sector using machine learning and deep learning methods 2022
- YAPAY SİNİR AĞLARINA DAYALI KISA DÖNEMLİ ELEKTRİK YÜKÜ TAHMİNİ 2021