Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
复制标题
通过低秩多核学习进行电力市场预测
DOI：
10.1109/jstsp.2014.2336611
复制
发表时间：
2013
影响因子：
7.5
通讯作者：
G. Giannakis
中科院分区：
文献类型：
--
作者：
V. Kekatos;Yu Zhang;G. Giannakis
智能电网愿景需要先进的信息技术和数据分析，以提高电网基础设施的效率、可持续性和经济性。为了实现这一目标，现代统计学习工具在这里被用来进行电力市场推断。日前价格预测是一个低秩核学习问题。独特地利用市场出清过程，拥塞模式建模为时空变化价格矩阵中的秩一分量。通过一种新的基于核范数的正则化，可以系统地选择跨定价节点和小时的内核。尽管从学习的角度来看，市场范围的预测是有益的，但它涉及到处理高维市场数据。后者成为可能后，设计了一个块坐标下降算法来解决所涉及的非凸优化问题。该算法利用块稀疏向量恢复的结果，并保证收敛到一个平稳点。从中西部ISO（MISO）市场的真实的数据的数值测试证实了预测精度，计算效率，和现有的替代品的开发方法的解释优点。
The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a low-rank kernel learning problem. Uniquely exploiting the market clearing process, congestion patterns are modeled as rank-one components in the matrix of spatio-temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours can be systematically selected. Even though market-wide forecasting is beneficial from a learning perspective, it involves processing high-dimensional market data. The latter becomes possible after devising a block-coordinate descent algorithm for solving the non-convex optimization problem involved. The algorithm utilizes results from block-sparse vector recovery and is guaranteed to converge to a stationary point. Numerical tests on real data from the Midwest ISO (MISO) market corroborate the prediction accuracy, computational efficiency, and the interpretative merits of the developed approach over existing alternatives.