Open Access

ARTICLE

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²

1 New Energy Technology Center, State Grid Beijing Electric Power Research Institute, Beijing, 100075, China
2 College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

* Corresponding Author: Qiong Wu. Email:

(This article belongs to the Special Issue: Innovative Energy Systems Management under the Goals of Carbon Peaking and Carbon Neutrality)

Energy Engineering 2024, 121(6), 1473-1493. https://doi.org/10.32604/ee.2024.047332

Received 02 November 2023; Accepted 16 January 2024; Issue published 21 May 2024

Abstract

Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data features. Subsequently, the moving average method is used for data preprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSO algorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed and accuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressing information loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According to the numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It can explore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods for the household load exhibiting significant fluctuations across different seasons.

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Keywords

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