一种基于深度学习的高能耗工业负荷分解鲁棒方法
A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning
摘要：
了解用户的用电模式是电力公司与用户在关键决策方面的重要协调机制。因此，负荷分解对于揭示负荷消耗及其特性之间的潜在关系至关重要。然而，传统上对住宅和商业负载执行负载分解，并且没有充分考虑高能耗工业负载，导致低效结果。因此，本文的重点是工业园区负荷分解（IPL）。然而，传统方法中常用的参数不适用于高能耗的工业负载。因此，开发了一种更稳健的方法，包括三种算法模型，以在IPL上实现这一目标。首先，采用改进的变分模式分解（IVMD）算法对IPL的训练数据进行去噪处理，提高IPL的稳定性。其次，利用卷积神经网络（CNN）和简单递归单元（SRU）联合算法，基于IPL特性，利用双层深度学习网络实现IPL的非侵入式、非侵入式分解过程。具体而言，CNN用于提取IPL数据特征，而改进的长短期记忆（LSTM）网络，SRU，采用开发的分解模型，并进一步训练负荷数据。通过鲁棒分解过程，提取负荷消耗中的潜在关系。数值算例的结果表明，该方法优于传统分解方法。
英文摘要：
The knowledge of the users' electricity consumption pattern is an important coordinating mechanism between the utility company and the electricity consumers in terms of key decision makings. The load decomposition is therefore crucial to reveal the underlying relationship between the load consumption and its characteristics. However, load decomposition is conventionally performed on the residential and commercial loads, and adequate consideration has not been given to the high-energy-consuming industrial loads leading to inefficient results. This paper thus focuses on the load decomposition of the industrial park loads (IPL). The commonly used parameters in a conventional method are however inapplicable in high-energy-consuming industrial loads. Therefore, a more robust approach is developed comprising a three-algorithm model to achieve this goal on the IPL. First, the improved variational mode decomposition (IVMD) algorithm is introduced to denoise the training data of the IPL and improve its stability. Secondly, the convolutional neural network (CNN) and simple recurrent units (SRU) joint algorithms are used to achieve a non-intrusive and non-invasive decomposition process of the IPL using a double-layer deep learning network based on the IPL characteristics. Specifically, CNN is used to extract the IPL data characteristics while the improved long and short-term memory (LSTM) network, SRU, is adopted to develop the decomposition model and further train the load data. Through the robust decomposition process, the underlying relationship in the load consumption is extracted. The results obtained from the numerical examples show that this approach outperforms the state-of-the-art in the conventional decomposition process.
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