Öz
Load forecasting is important in energy market. In fact electricity is a type of energy that cannot be stored, thus it is more important in electrical energy. The facilities need to balance between electricity generation and consumption by making plans. Computer-aided forecasting models are developed to reduce the effects of factors that disrupt this supply-demand balance. Generally, daily, weekly and monthly forecasts are made in demand forecast. In this study, hourly demand estimation is made. By using the past 24-hour consumption data and weather data such as temperature, humidity, wind speed and radiation in Konya, the next hour's consumption value was tried to forecast. Forecasting models were created using deep learning algorithms such as RNN, LSTM and GRU and the most successful model was determined by comparing the models.
Anahtar Kelimeler
Load forecasting Deep learning Time series Consumption of electricity Short-term
Kaynakça
- Aggarwal, C. C. (2018). Neural Networks and Deep Learning: A Textbook. In Artificial Intelligence, Springer Publishing. https://doi.org/10.1007/978-3-319-94463-0
- Choi, H., Ryu, S., & Kim, H. (2018, December 24). Short-Term Load Forecasting based on ResNet and LSTM. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018. https://doi.org/10.1109/SmartGridComm.2018.8587554
- Jiang, Q., Zhu, J. X., Li, M., & Qing, H. Y. (2018). Electricity Power Load Forecast via Long Short-Term Memory Recurrent Neural Networks. Proceedings - 2018 4th Annual International Conference on Network and Information Systems for Computers, ICNISC 2018, 265–268. https://doi.org/10.1109/ICNISC.2018.00060
- Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2019). Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, 10(1), 841–851. https://doi.org/10.1109/TSG.2017.2753802
- Mori, H., & Ogasawara, T. (1993). A recurrent neural network for short-term load forecasting. Proceedings of the 2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993, 395–400. https://doi.org/10.1109/ANN.1993.264315
- Nalbant, A., Aslan, Y., & Yaşar, C. (2005). Kütahya İli Elektrik Puant Yük Tahmini. Elektrik Elektronik Bilgisayar Mühendisliği, 11. Ulusal Kongresi, Bildiri Kitapçığı I, Sayfa:211-214 , İstanbul,
- Siddarameshwara, N., Yelamali, A., & Byahatti, K. (2010). Electricity short term load forecasting using Elman recurrent neural network. Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, 351–354. https://doi.org/10.1109/ARTCom.2010.44
- Tian, C., Ma, J., Zhang, C., & Zhan, P. (2018). A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies, 11(12). https://doi.org/10.3390/en11123493
- Tokgoz, A., & Unal, G. (2018). A RNN based time series approach for forecasting turkish electricity load. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, 1–4. https://doi.org/10.1109/SIU.2018.8404313
- Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017, May 10). Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network. 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017. https://doi.org/10.1109/CISS.2017.7926112
Öz
Kaynakça
- Aggarwal, C. C. (2018). Neural Networks and Deep Learning: A Textbook. In Artificial Intelligence, Springer Publishing. https://doi.org/10.1007/978-3-319-94463-0
- Choi, H., Ryu, S., & Kim, H. (2018, December 24). Short-Term Load Forecasting based on ResNet and LSTM. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018. https://doi.org/10.1109/SmartGridComm.2018.8587554
- Jiang, Q., Zhu, J. X., Li, M., & Qing, H. Y. (2018). Electricity Power Load Forecast via Long Short-Term Memory Recurrent Neural Networks. Proceedings - 2018 4th Annual International Conference on Network and Information Systems for Computers, ICNISC 2018, 265–268. https://doi.org/10.1109/ICNISC.2018.00060
- Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2019). Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, 10(1), 841–851. https://doi.org/10.1109/TSG.2017.2753802
- Mori, H., & Ogasawara, T. (1993). A recurrent neural network for short-term load forecasting. Proceedings of the 2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993, 395–400. https://doi.org/10.1109/ANN.1993.264315
- Nalbant, A., Aslan, Y., & Yaşar, C. (2005). Kütahya İli Elektrik Puant Yük Tahmini. Elektrik Elektronik Bilgisayar Mühendisliği, 11. Ulusal Kongresi, Bildiri Kitapçığı I, Sayfa:211-214 , İstanbul,
- Siddarameshwara, N., Yelamali, A., & Byahatti, K. (2010). Electricity short term load forecasting using Elman recurrent neural network. Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, 351–354. https://doi.org/10.1109/ARTCom.2010.44
- Tian, C., Ma, J., Zhang, C., & Zhan, P. (2018). A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies, 11(12). https://doi.org/10.3390/en11123493
- Tokgoz, A., & Unal, G. (2018). A RNN based time series approach for forecasting turkish electricity load. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, 1–4. https://doi.org/10.1109/SIU.2018.8404313
- Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017, May 10). Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network. 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017. https://doi.org/10.1109/CISS.2017.7926112
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2020 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 11 |
