用CNN和LSTM预测用电负荷与可再生能源，提升电网稳定性
Load and Renewable Energy Forecasting Using Deep Learning for Grid Stability
- 结合卷积神经网络与长短期记忆网络进行时序预测
- 相比传统方法，显著提升短时负荷与风光发电预测精度
- 适合电网调度、能源管理等实际工程场景应用
随着能源格局快速演变，电网运营商在整合可再生能源时面临诸多挑战，尤其是供需平衡问题，因太阳能和风能具有高度不确定性。在此背景下，可靠的短期负荷与可再生能源预测有助于稳定电网、最大化储能利用并保障可再生能源有效使用。以往该类预测多依赖物理模型与统计方法。近年来，机器学习与深度学习技术在可再生能源预测中展现出良好效果。具体而言，卷积神经网络（CNN）与长短期记忆网络（LSTM）以及传统机器学习如回归方法被广泛用于负荷与可再生能源预测任务。本文重点研究基于CNN与LSTM的预测方法。
原文摘要 · Abstract (English)
As the energy landscape changes quickly, grid operators face several challenges, especially when integrating renewable energy sources with the grid. The most important challenge is to balance supply and demand because the solar and wind energy are highly unpredictable. When dealing with such uncertainty, trustworthy short-term load and renewable energy forecasting can help stabilize the grid, maximize energy storage, and guarantee the effective use of renewable resources. Physical models and statistical techniques were the previous approaches employed for this kind of forecasting tasks. In forecasting renewable energy, machine learning and deep learning techniques have recently demonstrated encouraging results. More specifically, the deep learning techniques like CNN and LSTM and the conventional machine learning techniques like regression that are mostly utilized for load and renewable energy forecasting tasks. In this article, we will focus mainly on CNN and LSTM-based forecasting methods.
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