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http://dx.doi.org/10.3745/KTSDE.2020.9.12.419

Double Encoder-Decoder Model for Improving the Accuracy of the Electricity Consumption Prediction in Manufacturing  

Cho, Yeongchang ((주)에스더블유엠 부설연구소)
Go, Byung Gill ((주)에스더블유엠 부설연구소)
Sung, Jong Hoon ((주)에스더블유엠 부설연구소)
Cho, Yeong Sik ((주)AMEP 기술연구소)
Publication Information
KIPS Transactions on Software and Data Engineering / v.9, no.12, 2020 , pp. 419-430 More about this Journal
Abstract
This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.
Keywords
Time-Series Forecasting; Deep Learning; Machine Learning;
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1 J. Zheng, C. Xu, Z. Zhang, and X. Li, "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.
2 W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, "Short-term residential load forecasting based on LSTM recurrent neural network," IEEE Transactions on Smart Grid, Vol.10, No.1, pp.841-851, 2019.   DOI
3 J. Bedi and D. Toshniwal, "Deep learning framework to forecast electricity demand," Applied Energy, Vol.238, pp. 1312-1326, 2019.   DOI
4 R. K. Agrawal, F. Muchahary, and M. M. Tripathi, "Long term load forecasting with hourly predictions based on long-short-term-memory networks," 2018 IEEE Texas Power and Energy Conference (TPEC), 2018.
5 G. E. P. Box and G. M. Jenkins, Time series analysis: forecasting and control. Oakland: Holden-Day, 1976.
6 E. Erdogdu, "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Vol.35, No.2, pp.1129-1146, 2007.   DOI
7 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
8 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
9 G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
10 Y. Cho, Energy info. Korea. Kyonggi-do, Korea: Korea Energy Economics Institute, 2018.
11 E. E. Elattar, J. Goulermas, and Q. H. Wu, "Electric Load Forecasting Based on Locally Weighted Support Vector Regression," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol.40, No.4, pp.438-447, 2010.   DOI
12 Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le, "Sequence to sequence learning with neural networks," In Advances In Neural Information Processing Systems, pp.3104-3112. 2014.
13 K. Cho, B. V. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
14 I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. Cambridge, MA: MIT Press, 2017.
15 Hochreiter, Sepp, and Jurgen Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997.   DOI
16 G. Dudek, "Short-Term Load Forecasting Using Random Forests," Advances in Intelligent Systems and Computing Intelligent Systems 2014, pp.821-828, 2015.
17 T. He, Z. Dong, K. Meng, H. Wang, and Y. Oh, "Accelerating Multi-layer Perceptron based short term demand forecasting using Graphics Processing Units," 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009.
18 A. Graves, Supervised sequence labelling with recurrent neural networks. Berlin: Springer, 2012.
19 F. Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain," Psychological Review, Vol.65, No.6, pp.386-408, 1958.   DOI
20 M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function," Neural Networks, Vol.6, No.6, pp.861-867, 1993.   DOI
21 Y. Bengio, A. Courville, and P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.8, pp.1798-1828, 2013.   DOI
22 T. Luong, H. Pham, and C. D. Manning, "Effective Approaches to Attention-based Neural Machine Translation," Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.
23 D. Bahdanau, K. Cho, and Y. Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate," International Conference on Learning Representations, 2015.
24 J. Cheng, L. Dong, and M. Lapata, "Long Short-Term Memory-Networks for Machine Reading," Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
25 El Hihi, Salah, and Yoshua Bengio, "Hierarchical recurrent neural networks for long-term dependencies," In Advances in Neural Information Processing Systems, pp.493-499. 1996.
26 A. Parikh, O. Tackstrom, D. Das, and J. Uszkoreit, "A Decomposable Attention Model for Natural Language Inference," Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
27 L. Breiman, "Random Forests," Machine learning, Vol.45, No. 1, pp. 5-32, 2001.   DOI
28 Drucker, Harris, Christopher JC Burges, Linda Kaufman, Alex J. Smola, and Vladimir Vapnik, "Support vector regression machines," In Advances in Neural Information Processing Systems, pp. 155-161. 1997.
29 D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," 3rd International Conference for Learning Representations, 2015.
30 L. Bottou, "On-line Learning and Stochastic Approximations," On-Line Learning in Neural Networks, pp. 9-42, 1999.
31 T. Hastie, J. Friedman, and R. Tisbshirani, The Elements of statistical learning: data mining, inference, and prediction. New York: Springer, 2017.
32 D. Barber, Bayesian reasoning and machine learning. Cambridge: Cambridge University Press, 2018.
33 S.-Y. Shih, F.-K. Sun, and H.-Y. Lee, "Temporal pattern attention for multivariate time series forecasting," Machine Learning, Vol.108, No.8-9, pp.1421-1441, 2019.   DOI
34 G. Lai, W.-C. Chang, Y. Yang, and H. Liu, "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks," The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018.
35 Song, Fengxi, Zhongwei Guo, and Dayong Mei. "Feature selection using principal component analysis," In 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization, Vol.1, pp.27-30. IEEE, 2010.
36 M. Abadi, "TensorFlow: learning functions at scale," Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming - ICFP 2016, 2016.
37 Hall, Mark A. "Correlation-based Feature Selection for Machine Learning," PhD diss., The University of Waikato, 1999.