基于自适应多尺度注意力的跨异构数据集鲁棒能源和资源预测框架

Usman Gani Joy , Shahadat Kabir , A.F.M. Farhad , Asraful Islam
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引用次数: 0

摘要

准确的时间序列预测对于能源管理和资源分配至关重要,符合联合国可持续发展目标(SDG)关于可负担和清洁能源的目标(SDG 7)。然而,数据集的复杂性和异构性对现有模型提出了挑战,如主成分分析-变压器(PCA-Transformer),它依赖于静态技术,在特征提取、可扩展性和对不断变化的模式的适应性方面存在困难。该研究引入了一个集成小波多尺度变换(WIMST)、自适应变换(AT)、残差块(RB)和注意机制(AM)的框架。WIMST动态提取多尺度特征,AT模型复杂的相互作用,RB稳定深度训练,AM捕获远程依赖关系,协同解决动态数据集中的非线性模式。在加州大学欧文分校(UCI)电器能源和美国能源信息管理局(EIA)可再生能源数据集上进行评估,该模型在UCI上的均方根误差(RMSE)为0.1899,平均绝对误差(MAE)为0.1478,决定系数(R2)为0.9998(基线RMSE为0.35 - 14.39),在EIA上的RMSE值为0.4393(水电),0.3093(废物),2.9767(太阳能)和0.1081(地热)。与自回归综合移动平均(ARIMA)相比,误差减少了70%-97%。这些结果突出了跨不同应用的能源预测的优越准确性和稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive multi-scale attention-based framework for robust energy and resource forecasting across heterogeneous datasets
Accurate time series forecasting is crucial for energy management and resource allocation, aligning with the United Nations Sustainable Development Goals (SDGs) for affordable and clean energy (SDG 7). However, dataset complexity and heterogeneity challenge existing models, such as Principal Component Analysis-Transformer (PCA-Transformer), which rely on static techniques and struggle with feature extraction, scalability, and adaptability to evolving patterns. This study introduces a framework integrating a Wavelet-Inspired Multi-Scale Transform (WIMST), Adaptive Transform (AT), Residual Blocks (RB), and Attention Mechanism (AM). The WIMST dynamically extracts multi-scale features, AT models complex interactions, RB stabilize deep training, and AM captures long-range dependencies, synergistically addressing non-linear patterns in dynamic datasets. Evaluated on the University of California, Irvine (UCI) Appliances Energy and U.S. Energy Information Administration (EIA) Renewable Energy datasets, the model achieves a Root Mean Square Error (RMSE) of 0.1899, Mean Absolute Error (MAE) of 0.1478, and coefficient of determination (R2) of 0.9998 on UCI (versus baselines’ RMSE of 0.350–14.39), and RMSE values of 0.4393 (Hydro), 0.3093 (Waste), 2.9767 (Solar), and 0.1081 (Geothermal) on EIA, reducing errors by 70%–97% compared to Autoregressive Integrated Moving Average (ARIMA). These results highlight superior accuracy and robustness for energy forecasting across diverse applications.
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