用多变量LSTM预测风光发电，提升电网稳定性和减排效果
Multivariate LSTM-Based Forecasting for Renewable Energy: Enhancing Climate Change Mitigation
- 基于多变量LSTM建模风光发电的长期依赖与跨区域互动
- 实测显示该方法降低碳排放并提高电力供应可靠性
- 适合关注可再生能源调度与碳中和的能源研究者
可再生能源（RES）在现代电力系统中的集成带来机遇与挑战，主要源于其发电的固有波动性。准确预测可再生能源发电量对保障电力系统可靠性、稳定性及经济效率至关重要。传统方法如确定性模型与随机规划常依赖聚类技术（如K-means）生成代表性场景，但难以充分捕捉可再生能源数据中的复杂时序依赖与非线性模式。本文提出一种多变量长短期记忆（LSTM）网络，利用真实历史数据进行可再生能源发电预测。该模型有效捕捉不同可再生能源间的长期依赖关系及相互作用，结合本地与邻近区域的历史数据，显著提升预测精度。案例研究表明，该方法可降低二氧化碳排放，并实现更可靠的电力负荷供应。
原文摘要 · Abstract (English)
The increasing integration of renewable energy sources (RESs) into modern power systems presents significant opportunities but also notable challenges, primarily due to the inherent variability of RES generation. Accurate forecasting of RES generation is crucial for maintaining the reliability, stability, and economic efficiency of power system operations. Traditional approaches, such as deterministic methods and stochastic programming, frequently depend on representative scenarios generated through clustering techniques like K-means. However, these methods may fail to fully capture the complex temporal dependencies and non-linear patterns within RES data. This paper introduces a multivariate Long Short-Term Memory (LSTM)-based network designed to forecast RESs generation using their real-world historical data. The proposed model effectively captures long-term dependencies and interactions between different RESs, utilizing historical data from both local and neighboring areas to enhance predictive accuracy. In the case study, we showed that the proposed forecasting approach results in lower CO2 emissions, and a more reliable supply of electric loads.
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