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


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 12, pp. 419-430, Dec. 2020
https://doi.org/10.3745/KTSDE.2020.9.12.419,   PDF Download:
Keywords: Time-Series Forecasting, Deep Learning, Machine Learning
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.


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Cite this article
[IEEE Style]
Y. Cho, B. G. Go, J. H. Sung, Y. S. Cho, "Double Encoder-Decoder Model for Improving the Accuracy of the Electricity Consumption Prediction in Manufacturing," KIPS Transactions on Software and Data Engineering, vol. 9, no. 12, pp. 419-430, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.12.419.

[ACM Style]
Yeongchang Cho, Byung Gill Go, Jong Hoon Sung, and Yeong Sik Cho. 2020. Double Encoder-Decoder Model for Improving the Accuracy of the Electricity Consumption Prediction in Manufacturing. KIPS Transactions on Software and Data Engineering, 9, 12, (2020), 419-430. DOI: https://doi.org/10.3745/KTSDE.2020.9.12.419.