EN
TR
A DEEP LEARNING-BASED DEMAND FORECASTING SYSTEM FOR PLANNING ELECTRICITY GENERATION
Abstract
In today's world, where economic and industrial development continues, the importance of electrical energy is constantly increasing. Energy demand should be forecast as precisely as possible to reduce lost energy costs in the system, to plan generation expenditures appropriately, to ensure that market players are not economically harmed, and to deliver quality and uninterrupted energy to system consumers. Balancing the electric energy supply and demand of the system is possible with a forecasting plan. Our research aims to generate hourly electricity consumption load forecasts for the period 2018-2021 using Turkish Electricity Consumption Data and meteorological data, with the addition of time and public holiday features. The forecasting performance of the models is evaluated by training multiple machine learning models and deep neural network-based time series models with the data. When the prediction results of our load demand forecasting problem were evaluated, it was seen that deep learning methods gave higher results in prediction success compared to machine learning models. It has been observed that the prediction success of the LSTM model, one of the deep learning methods we use, is higher than the RNN and GRU models. The analysis envisages the elimination of mismatches between energy supply and demand.
Keywords
Kaynakça
- Aguilar Madrid, E. & Antonio, N.(2021). Short-term electricity load forecasting with machine learning. Information, 12(2), 50. https://doi.org/10.3390/info12020050.
- Amjady, N. (2001). Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Transactions on power systems, 16(3), 498-505. https://doi.org/10.1109/59.932287.
- Aydogan, M. (2019). Performing Text Analysis Using Deep Learning Algorithms in Big Data. Doctoral Thesis. Inonu University Institute of Science and Technology, Malatya.
- Banik, R., Das, P., Ray, S. & Biswas, A. (2021). Prediction of electrical energy consumption based on machine learning technique. Electrical Engineering, 103(2) , 909-920. https://doi.org/10.1007/s00202-020-01126-z.
- Biskin, O.T. & Çifci, A. (2021). Forecasting of Turkey’s Electrical Energy Consumption using LSTM and GRU Networks. Bilecik Şeyh Edebali University Journal of Science and Technology, 8(2), 656-667. https://doi.org/10.35193/bseufbd.935824.
- Chan, S., Oktavianti, I. & Puspita, V. (2019). A deep learning cnn and ai-tuned svm for electricity consumption forecasting: Multivariate time series data. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 0488-0494. IEEE. https://doi.org/10.1109/IEMCON.2019.8936260.
- Cunkas, M. & Altun, A.A. (2010). Long term electricity demand forecasting in Turkey using artificial neural networks. Energy Sources, Part B: Economics, Planning, and Policy, 5(3) , 279-289. https://doi.org/10.1080/15567240802533542.
- Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Derin Öğrenme
Bölüm
Araştırma Makalesi
Yazarlar
Erkan Duman
0000-0003-2439-7244
Türkiye
Yayımlanma Tarihi
3 Haziran 2024
Gönderilme Tarihi
1 Aralık 2023
Kabul Tarihi
26 Mart 2024
Yayımlandığı Sayı
Yıl 1970 Cilt: 27 Sayı: 2
APA
Gökçe, M. M., & Duman, E. (2024). A DEEP LEARNING-BASED DEMAND FORECASTING SYSTEM FOR PLANNING ELECTRICITY GENERATION. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 511-522. https://doi.org/10.17780/ksujes.1399160
Cited By
Analyzing the Effect of Error Estimation on Random Missing Data Patterns in Mid-Term Electrical Forecasting
Electronics
https://doi.org/10.3390/electronics14071383An integrated multiscale computational framework deciphers SARS-CoV-2 resistance to sotrovimab
Biophysical Journal
https://doi.org/10.1016/j.bpj.2025.05.015PORTEKİZ GÜN ÖNCESİ ELEKTRİK FİYATLARININ DERİN ÖĞRENME YÖNTEMİYLE TAHMİNİ: ZAMAN SERİLERİ İÇİN SİNİRSEL TEMEL GENİŞLEME ANALİZİ MODELİ
Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.17780/ksujes.1736690MELTBLOWN MAKİNELERİNDE ÜRETİLEN DOKUSUZ KUMAŞLARIN BASINÇ VERİMLİLİĞİNİN MAKİNE ÖĞRENMESİ YÖNTEMLERİ İLE TAHMİNLENMESİ VE PERFORMANS ANALİZİ
Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.17780/ksujes.1747540