- Volume 9 Issue 5
DOI QR Code
Deep Neural Network Model For Short-term Electric Peak Load Forecasting
단기 전력 부하 첨두치 예측을 위한 심층 신경회로망 모델
- Hwang, Heesoo (Department of Electrical and Electronic Engineering, Halla University)
- 황희수 (한라대학교 전기전자공학과)
- Received : 2018.03.08
- Accepted : 2018.05.20
- Published : 2018.05.28
In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).
- A. J. Al-Shareef, E. A. Mohamed & E. Al-Judaibi. (2008). Next 24-hours Load Forecasting Using Artificial Neural Network for the Western Area of Saudi Arabia, JKAU: Eng. Sci., 19(2), 25-40. https://doi.org/10.4197/Eng.19-2.2
- B. H. Wang. (2009). Short-term Electrical Load Forecasting Using Neuro-fuzzy Model with Error Compensation, International Journal of Fuzzy Logic and Intelligent Systems, 9(4), 249-342. https://doi.org/10.5391/IJFIS.2009.9.4.249
- C. Ying, P. B. Luh, G. Che, Z. Yige, L. D. Michel, M. A. Coolbeth, P. B. Friedland & S. J Rourke. (2010). Short-term Load Forecasting: Similar Day-based Wavelet Neural Networks, IEEE Trans. on Power Systems, 25(1), 322-330. https://doi.org/10.1109/TPWRS.2009.2030426
- L. M. Saini. (2008). Peak Load Forecasting Using Bayesian Regularization, Resilient and Adaptive Backpropagation Learning Based Artificial Neural Networks, Electric Power Systems Research 78, 1302-1310. https://doi.org/10.1016/j.epsr.2007.11.003
- K. K. Seo. (2015). Sales Prediction of Electronic Appliances Using A Convergence Model Based on Artificial Neural Network and Genetic, Journal of digital Convergence, 13(9), 177-182. https://doi.org/10.14400/JDC.2015.13.9.177
- R. R. Agnaldo & P. A. Alexandre. (2005). Feature Extraction via Multi-resolution Analysis for Short-term Load Forecasting, IEEE Trans. Power Systems, 20(1), 189-198. https://doi.org/10.1109/TPWRS.2004.840380
- S. Fan & L. Chen. (2006). Short-term Load Forecasting Based on An Adaptive Hybrid Method, IEEE Trans. Power Systems, 23(1), pp. 392-401.
- T. Senjyu, P. Mandal, K. Uezato & T. Funabashi. (2004). Next Day Load Curve Forecasting Using Recurrent Neural Network Structure, IEE Proc.- Gener. Transm. and Distrib., 151(3), 388-394. https://doi.org/10.1049/ip-gtd:20040356
- Z. Yun, Z. Quan, S. Caixin, L. Shaolan, L. Yuming & S. Yang. (2008). RBF Neural Network and ANFIS-based Short-term Load Forecasting Approach in Real-Time Price Environment, IEEE Trans. on Power Systems, 23(3), 853-858. https://doi.org/10.1109/TPWRS.2008.922249
- T. Ouyang, Y. He, H. Li, Z. Sun & S. Baek. A Deep Learning Framework for Short-term Power Load Forecasting, https://arxiv.org/pdf/1711.11519.
- T. Hossen, S. J. Plathottam & R. K. Angamuthu. (2017). Short-term Load Forecasting Using Deep Neural Networks (DNN), Power Symposium (NAPS), North American, 17-19.
- K. Amarasinghe, D. L. Marino & M. Manic. (2017). Deep Neural Networks for Energy Load Forecasting, Industrial Electronics (ISIE), 2017 IEEE 26th International Symposium, 19-21.
- Wan He. (2017). Load Forecasting via Deep Neural Networks, Procedia Computer Science, 122, 308-314. https://doi.org/10.1016/j.procs.2017.11.374
- M. F. Moller. (1993). A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, 6, 525-533. https://doi.org/10.1016/S0893-6080(05)80056-5
- H. S. Hwang. (2013). Daily Electric Load Forecasting Based on RBF Neural Network Models, International Journal of Fuzzy Logic and Intelligent Systems, 13(1), 39-49. https://doi.org/10.5391/IJFIS.2013.13.1.39
- J. H. Lim, S. Y. Kim, J. D. Park & K. B. Song. (2013). Representative Temperature Assessment for Improvement of Short-Term Load Forecasting Accuracy, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 27(6), 39-43. https://doi.org/10.5207/JIEIE.2013.27.6.039
- O. S. Kwon & K. B. Song. (2011). Development of Short-Term Load Forecasting Method by Analysis of Load Characteristics During Chuseok Holiday, The transactions of The Korean Institute of Electrical Engineers 60(12), 2215-2220. https://doi.org/10.5370/KIEE.2011.60.12.2215
- S. Y. Kim, J. H. Lim, J. D. Park & K. B. Song. (2013). Short-Term Electric Load Forecasting for the Consecutive Holidays Using the Power Demand Variation Rate, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 27(6), 17-22. https://doi.org/10.5207/JIEIE.2013.27.6.017
- P. Zhang, X. Wu, X. Wang & S. Bi. (2015). Short-Term Load Forecasting Based on Big Data Technologies, CSEE Journal of Power And Energy Systems, 1(3). 59-67. https://doi.org/10.17775/CSEEJPES.2015.00036
- H. Zhao, Z. Tang, W. Shi & Z. Wang. (2017). Study of Short-term Load Forecasting In Big Data Environment, Control And Decision Conference, 28-30. DOI: 10.1109/CCDC.2017.7978378. https://doi.org/10.1109/CCDC.2017.7978378