A highly accurate electricity price prediction model is of the utmost importance for multiple power systems tasks, such as generation dispatch and bidding. Due to the liberalization of the electricity market, as well as high renewable penetration, the properties of electricity price time series are becoming more stochastic and complex. Traditional statistical methods and machine learning algorithms cannot model such volatile market conditions with high fidelity. In this paper, we propose a data-driven deep learning network (GHTnet) to capture the temporal distribution of real-time price data. A new CNN module, based on GoogLeNet, is developed to capture the high-frequency features of this data, while inclusion of time series summary statistics is shown to improve the forecasting of volatile price spikes. The deep learning model is developed and validated on real-time price time series from 49 generators in the New York Independent System Operator (NYISO), achieving significant performance improvements over that of state-of-the-art benchmark methods, with an average 17.34% improvement in MAPE.

高度准确的电价预测模型对于发电调度和招标等多个电力系统任务至关重要。由于电力市场的开放以及可再生能源的高渗透率，电价时间序列的特性变得更加随机和复杂。传统的统计方法和机器学习算法无法高保真地模拟这种动荡的市场状况。在本文中，我们提出了一个数据驱动的深度学习网络（GHTnet）来捕捉实时价格数据的时间分布。开发了一个基于 GoogLeNet 的新 CNN 模块来捕获该数据的高频特征，同时显示包含时间序列汇总统计数据以改进对波动价格峰值的预测。