While non-parametric models, such as neural networks, are sufficient in the load forecasting, separate estimates of fixed and shiftable loads are beneficial to a wide range of applications such as distribution system operational planning, load scheduling, energy trading, and utility demand response programs. A semi-parametric estimation model is usually required, where cost sensitivities of demands must be known. Existing research work consistently uses somewhat arbitrary parameters that seem to work best. In this paper, we propose a generic class of data-driven semiparametric models derived from consumption data of residential consumers. A two-stage machine learning approach is developed. In the first stage, disaggregation of the load into fixed and shiftable components is accomplished by means of a hybrid algorithm consisting of non-negative matrix factorization (NMF) and Gaussian mixture models (GMM), with the latter trained by an expectation-maximization (EM) algorithm. The fixed and shiftable loads are subject to analytic treatment with economic considerations. In the second stage, the model parameters are estimated using an L2-norm, epsilon-insensitive regression approach. Actual energy usage data of two residential customers show the validity of the proposed method.

中文翻译：

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一种数据驱动的机器学习方法，用于带负载分解的消费者建模

虽然非参数模型（如神经网络）在负荷预测中就足够了，固定和可移动负荷的单独估计有利于广泛的应用，如配电系统运营规划、负荷调度、能源交易和公用事业需求响应程式。通常需要半参数估计模型，其中必须知道需求的成本敏感性。现有的研究工作始终使用看起来效果最好的任意参数。在本文中，我们提出了一类基于住宅消费者消费数据的数据驱动半参数模型。开发了一种两阶段机器学习方法。在第一阶段，将负载分解为固定和可移动分量是通过由非负矩阵分解 (NMF) 和高斯混合模型 (GMM) 组成的混合算法完成的，后者由期望最大化 (EM) 算法训练。固定和可移动载荷受经济考虑进行分析处理。在第二阶段，使用 L2 范数、ε 不敏感回归方法估计模型参数。两个居民用户的实际能源使用数据表明了所提出方法的有效性。模型参数是使用 L2 范数、epsilon 不敏感回归方法估计的。两个居民用户的实际能源使用数据表明了所提出方法的有效性。模型参数是使用 L2 范数、epsilon 不敏感回归方法估计的。两个居民用户的实际能源使用数据表明了所提出方法的有效性。