DelayNet: Enhancing Temporal Feature Extraction for Electronic Consumption Forecasting with Delayed Dilated Convolution

Le Hoang Anh, Gwang-Hyun Yu, Dang Thanh Vu, Hyoung-Gook Kim and Jin-Young Kim ()
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Le Hoang Anh: Department of ICT Convergence System Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea
Gwang-Hyun Yu: Department of ICT Convergence System Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea
Dang Thanh Vu: Department of ICT Convergence System Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea
Hyoung-Gook Kim: Department of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of Korea
Jin-Young Kim: Department of ICT Convergence System Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea

Energies, 2023, vol. 16, issue 22, 1-18

Abstract: In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater computational demands. In this research, on the other hand, we introduce DelayNet, a lightweight deep learning model that maintains model efficiency while accommodating extended time sequences. Our DelayNet is designed based on the observation that electronic series data exhibit recurring irregular patterns over time. Furthermore, we present two substantial datasets of electricity consumption records from South Korean buildings spanning nearly two years. Empirical findings demonstrate the model’s performance, achieving 21.23%, 43.60%, 17.05% and 21.71% improvement compared to recurrent neural networks, gated-recurrent units, temporal convolutional neural networks and ARIMA models, as well as greatly reducing model complexity and computational requirements. These findings indicate the potential for micro-level power consumption planning, as lightweight models can be implemented on edge devices.

Keywords: temporal convolutional neural Network; time-delay; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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