Araştırma Makalesi

Machine Learning Based Short Term Load Estimation in Commercial Buildings

Cilt: 5 Sayı: 2 31 Aralık 2021
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Machine Learning Based Short Term Load Estimation in Commercial Buildings

Öz

Nowadays, there are many problems with the electricity system, such as increasing consumption, short-time overload during the intra-day, environmental problems caused by fossil fuel, and foreign-source dependency. Therefore, it is necessary to meet these increasing energy needs, minimize environmental impacts, and develop cost optimization solutions. In order to meet these requirements, it is necessary for the network to have a more dynamic structure and to have real-time monitoring and control systems. Furthermore, to develop the aforementioned system, it is necessary to estimate the load of the users in the system. Therefore, the developed artificial neural network-based load estimation algorithm is capable of high accuracy load estimates, and high precision data were obtained for use in the demand side management system

Anahtar Kelimeler

Kaynakça

  1. Aghajani, G. R., Shayanfar, H. A., & Shayeghi, H. (2017). Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy. https://doi.org/10.1016/j.energy.2017.03.051
  2. Bendaoud, N. M. M., & Farah, N. (2020). Using deep learning for short-term load forecasting. Neural computing and applications, 32(18), 15029-15041.
  3. Berriel, R. F., Lopes, A. T., Rodrigues, A., Varejao, F. M., & Oliveira-Santos, T. (2017, May). Monthly energy consumption forecast: A deep learning approach. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 4283-4290). IEEE.
  4. Bruno, S., Dellino, G., La Scala, M., & Meloni, C. (2018). A Microforecasting Module for Energy Consumption in Smart Grids. Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. https://doi.org/10.1109/EEEIC.2018.8494345
  5. Chitalia, G., Pipattanasomporn, M., Garg, V., & Rahman, S. (2020). Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. Applied Energy, 278, 115410.
  6. Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., ... & Wang, K. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659-670.
  7. Chernykh, I., Chechushkov, D., & Panikovskaya, T. (2014). The prediction of electric energy consumption using an artificial neural network. WIT Transactions on Ecology and the Environment, 190 VOLUME 1, 109–117. https://doi.org/10.2495/EQ140121
  8. Fujiwara, T., & Ueda, Y. (2018). Load forecasting method for Commercial facilities by determination of working time and considering weather information. 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018, 5, 336–341. https://doi.org/10.1109/ICRERA.2018.8567019

Ayrıntılar

Birincil Dil

İngilizce

Konular

Elektrik Mühendisliği

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

31 Aralık 2021

Gönderilme Tarihi

21 Ekim 2021

Kabul Tarihi

15 Aralık 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 5 Sayı: 2

Kaynak Göster

APA
Erten, M. Y., & İnanç, N. (2021). Machine Learning Based Short Term Load Estimation in Commercial Buildings. International Scientific and Vocational Studies Journal, 5(2), 171-181. https://doi.org/10.47897/bilmes.1033438
AMA
1.Erten MY, İnanç N. Machine Learning Based Short Term Load Estimation in Commercial Buildings. ISVOS. 2021;5(2):171-181. doi:10.47897/bilmes.1033438
Chicago
Erten, Mustafa Yasin, ve Nihat İnanç. 2021. “Machine Learning Based Short Term Load Estimation in Commercial Buildings”. International Scientific and Vocational Studies Journal 5 (2): 171-81. https://doi.org/10.47897/bilmes.1033438.
EndNote
Erten MY, İnanç N (01 Aralık 2021) Machine Learning Based Short Term Load Estimation in Commercial Buildings. International Scientific and Vocational Studies Journal 5 2 171–181.
IEEE
[1]M. Y. Erten ve N. İnanç, “Machine Learning Based Short Term Load Estimation in Commercial Buildings”, ISVOS, c. 5, sy 2, ss. 171–181, Ara. 2021, doi: 10.47897/bilmes.1033438.
ISNAD
Erten, Mustafa Yasin - İnanç, Nihat. “Machine Learning Based Short Term Load Estimation in Commercial Buildings”. International Scientific and Vocational Studies Journal 5/2 (01 Aralık 2021): 171-181. https://doi.org/10.47897/bilmes.1033438.
JAMA
1.Erten MY, İnanç N. Machine Learning Based Short Term Load Estimation in Commercial Buildings. ISVOS. 2021;5:171–181.
MLA
Erten, Mustafa Yasin, ve Nihat İnanç. “Machine Learning Based Short Term Load Estimation in Commercial Buildings”. International Scientific and Vocational Studies Journal, c. 5, sy 2, Aralık 2021, ss. 171-8, doi:10.47897/bilmes.1033438.
Vancouver
1.Mustafa Yasin Erten, Nihat İnanç. Machine Learning Based Short Term Load Estimation in Commercial Buildings. ISVOS. 01 Aralık 2021;5(2):171-8. doi:10.47897/bilmes.1033438

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