Szczegóły

Tytuł artykułu

Grid Search of Convolutional Neural Network model in the case of load forecasting

Tytuł czasopisma

Archives of Electrical Engineering

Afiliacje

Tran, Thanh Ngoc : Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao, Ward 4, Go Vap District, Ho Chi Minh City, Vietnam

Autorzy

Słowa kluczowe

load forecasting ; Grid Search ; Convolutional Neural Network

Wydział PAN

Nauki Techniczne

Zakres

25-30

Wydawca

Polish Academy of Sciences

Bibliografia

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[4] Walther J., Spanier D., Panten N., Abele E., Very short-term load forecasting on factory level – A machine learning approach, Procedia CIRP, vol. 80, pp. 705–710 (2019).
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[6] Joshi M., Singh R., Short-term load forecasting approaches: A review, International Journal of Recent Engineering Research and Development (IJRERD), no. 01, pp. 9–17 (2015).
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[8] Yu Y., Ji T.Y., Li M.S., Wu Q.H., Short-term Load Forecasting Using Deep Belief Network with Empirical Mode Decomposition and Local Predictor, 2018 IEEE Power and Energy Society General Meeting (PESGM), Portland, OR, pp. 1–5 (2018).
[9] Yang J.,Wang Q., A Deep Learning Load Forecasting Method Based on Load Type Recognition, 2018 International Conference on Machine Learning and Cybernetics (ICMLC), Chengdu, pp. 173–177 (2018).
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[17] Chen K., Chen K., Wang Q., He Z., Hu J., He J., Short-Term Load Forecasting With Deep Residual Networks, IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3943–3952 (2019).
[18] Jojo Moolayil, Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python, Apress (2018).
[19] Xishuang Dong, Lijun Qian, Lei Huang, Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach, IEEE Int. Conf. Big Data Smart Comput., pp. 119–125 (2017).
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[21] Voß M., Bender-Saebelkampf C., Albayrak S., Residential Short-Term Load Forecasting Using Convolutional Neural Networks, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aalborg, 2018, pp. 1–6 (2018).
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[23] Koprinska I., Wu D., Wang Z., Convolutional Neural Networks for Energy Time Series Forecasting, Proc. Int. Jt. Conf. Neural Networks, pp. 1–8 (2018), DOI: 10.1109/IJCNN.2018.8489399.
[24] Valentino Zocca et al., Python Deep Learning, Packt Publishing (2019).
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[26] Haiqing Liu, Weijian Lin, Yuancheng Li, Ultra-short-term wind power prediction based on copula function and bivariate EMD decomposition algorithm, Archives of Electrical Engineering, vol. 69, no. 2, pp. 271–286 (2020).
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Data

2021.03.25

Typ

Article

Identyfikator

DOI: 10.24425/aee.2021.136050 ; ISSN: 1427-4221 ; eISSN: 2300-2506
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