论文标题
在电力市场中学习日间和实时地点边际价格的差距
Learning the Gap in the Day-Ahead and Real-Time Locational Marginal Prices in the Electricity Market
论文作者
论文摘要
在本文中，统计机器学习算法以及深度神经网络用于预测日前和实时电力市场之间价格差距的值。收集了几种外源特征，并检查了这些特征的影响，以捕获特征与目标变量之间的最佳关系。集合学习算法，即发行的随机森林，以计算预测的日期和实时市场的预测电价的概率分布。长期 - 内存（LSTM）用于捕获长期依赖性，以预测上述市场之间的直接差距值和直接预测差距价格的收益，而不是减去对日前和实时市场的预测。在两年内，对加利福尼亚独立系统运营商（CAISO）电力市场数据进行了案例研究。评估所提出的方法，并在预测间隙的确切值方面显示出令人鼓舞的结果。
In this paper, statistical machine learning algorithms, as well as deep neural networks, are used to predict the values of the price gap between day-ahead and real-time electricity markets. Several exogenous features are collected and impacts of these features are examined to capture the best relations between the features and the target variable. Ensemble learning algorithm namely the Random Forest issued to calculate the probability distribution of the predicted electricity prices for day-ahead and real-time markets. Long-Short-Term-Memory (LSTM) is utilized to capture long term dependencies in predicting direct gap values between mentioned markets and the benefits of directly predicting the gap price rather than subtracting the predictions of day-ahead and real-time markets are illustrated. Case studies are implemented on the California Independent System Operator (CAISO) electricity market data for a two years period. The proposed methods are evaluated and neural networks showed promising results in predicting the exact values of the gap.
