Residential load forecasting based on symplectic geometry mode decomposition and GRU neural network with attention mechanism

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2024-04-16 DOI:10.1002/asmb.2861
Yuting Lu, Gaocai Wang, Xianfei Huang, Man Wu
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Abstract

Short-term residential load forecasting plays an increasingly important role in modern smart grids, with its main challenge being the high volatility and uncertainty of load curves. This article proposes a hybrid Symplectic Geometry Mode Decomposition-Gated Recurrent Unit with Attention Mechanism (SGMD-GRUAM) model for hourly residential load forecasting. First, SGMD is used to decompose the residential load and obtain a series of stable subsequences. Then, the Pearson correlation coefficient is used to select features related to each subsequence, such as weather factors. Next, a GRUAM prediction model is constructed for each subsequence. Finally, the final load prediction value is obtained by superimposing the previous component sequences and eliminating the noise sequence. The experiment uses the public dataset from UMass for a case study and compares it with benchmark models such as ARIMA and EEDM-GRUAM. The experimental results show that the proposed SGMD-GRUAM model has significant advantages in terms of prediction performance.
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基于交映几何模式分解和 GRU 神经网络与关注机制的居民负荷预测
短期居民负荷预测在现代智能电网中发挥着越来越重要的作用,其主要挑战在于负荷曲线的高波动性和不确定性。本文提出了一种用于每小时居民负荷预测的混合交映几何模式分解-带注意机制的门控循环单元(SGMD-GRUAM)模型。首先,利用 SGMD 对居民负荷进行分解,得到一系列稳定的子序列。然后,利用皮尔逊相关系数选择与每个子序列相关的特征,如天气因素。然后,为每个子序列构建 GRUAM 预测模型。最后,通过叠加之前的分量序列并剔除噪声序列,得到最终的负载预测值。实验以马萨诸塞大学的公共数据集为案例,并将其与 ARIMA 和 EEDM-GRUAM 等基准模型进行比较。实验结果表明,所提出的 SGMD-GRUAM 模型在预测性能方面具有显著优势。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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