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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

General Research Article

TCN Multi-time-scale Transformation and Temporal-Attention Neural Network for Monthly Electricity Consumption Forecasting

Author(s): Hao Hu*, Wen Jie Li, Yan Shi, Chao Zhou and DeHua Guo

Volume 16, Issue 8, 2023

Published on: 30 May, 2023

Page: [872 - 883] Pages: 12

DOI: 10.2174/2352096516666230418104618

Price: $65

Abstract

Background: For the efficient and secure running of the power industry, accurate monthly electricity projections are crucial. Due to coupling variations and a variety of data resolutions, current approaches are still unable to accurately extract multidimensional time-series data.

Objective: For monthly electricity consumption forecasting, a multi-time-scale transformation and temporal attention neural network for a temporal convolutional network is proposed.

Methods: First, a multi-time-scale compression model of temporal convolutional network is proposed, which compresses data on several time scales from different resolutions, such as the economy, weather, and historical load. Second, a multi-source temporal attention module is built to further dynamically extract crucial information. Finally, the decoding-encoding and residual connections' structure contributes to the prediction's improved resilience.

Results: The proposed method was compared with the state-of-the-art monthly load forecasting based on two years of historical data in a certain region, demonstrating its effectiveness.

Conclusion: Through the verification of local historical data, the proposed model was contrasted with cutting-edge monthly load forecasting techniques. The obtained results demonstrate the effectiveness.

Keywords: Monthly electricity consumption forecasting, multi-time-scale transformation, temporal attention, decodingencoding, TCN, multidimensional time-series data.

Graphical Abstract
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