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Multi-energy load forecasting for small-sample integrated energy systems based on neural network Gaussian process and multi-task learning
Energy Conversion and Management ( IF 9.9 ) Pub Date : 2024-09-14 , DOI: 10.1016/j.enconman.2024.119027
Wei Zhang , Yi Cai , Hongyi Zhan , Mao Yang , Wei Zhang

Multi-energy load forecasting forms the foundation of the operation and scheduling of integrated energy systems. Nevertheless, insufficient data and underutilization of the coupling relationship between the multi-energy load limit the accuracy of load forecasting. This paper presents a predictive model combining neural network Gaussian processes and multi-task learning. The approach is tailored to enhance forecasting accuracy in environments with small-sample datasets. This model capitalizes on the advantageous properties of infinitely wide neural networks for handling small-sample data. Simultaneously, the model effectively extracts the interconnected dynamics of cooling, heating, and electricity loads within the integrated energy system through multi-task learning. In addition, the model applies concrete dropout, enhancing robustness to irregular loads while maintaining the synergistic benefits of the multi-task framework. Furthermore, this paper employs a two-stage gradient descent approach to replace kernel matrix computations of Gaussian processes, reducing the computational cost of parameter optimization and yielding superior forecasting performance in shorter training durations. The simulation results indicate that the proposed model attains a mean accuracy of 97.93% for a 3-day forecasting horizon. Compared with alternative forecasting models, this model exhibits higher accuracy and enhanced generalization capabilities in multi-energy load forecasting for small-sample integrated energy systems.

中文翻译:


基于神经网络高斯过程和多任务学习的小样本综合能源系统多能量负荷预测



多能源负荷预测构成了集成能源系统运行和调度的基础。然而,数据不足和多能负荷耦合关系利用不足限制了负荷预测的准确性。本文提出了一个结合了神经网络高斯过程和多任务学习的预测模型。该方法经过定制,可在具有小样本数据集的环境中提高预测准确性。该模型利用了无限宽神经网络的优点来处理小样本数据。同时,该模型通过多任务学习有效地提取了综合能源系统内冷、热和电负荷的相互关联动态。此外,该模型还应用了混凝土 dropout,增强了对不规则载荷的稳健性,同时保持了多任务框架的协同优势。此外,该文采用两阶段梯度下降方法来代替高斯过程的核矩阵计算,降低了参数优化的计算成本,并在较短的训练时间内产生了卓越的预测性能。仿真结果表明,所提模型在 3 天预测范围内的平均准确率为 97.93%。与其他预测模型相比,该模型在小样本综合能源系统的多能源负荷预测中表现出更高的准确性和增强的泛化能力。
更新日期:2024-09-14
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