多变量LSTM基于预测可再生能源：增强气候变化缓解
Multivariate LSTM-Based Forecasting for Renewable Energy: Enhancing Climate Change Mitigation
AI总结：
本文提出基于多变量LSTM的预测模型，用于提高可再生能源发电的预测精度，从而降低碳排放并提升电力供应的可靠性。
摘要：
可再生能源（RES）日益融入现代电力系统，带来了显著机遇，但也带来了显著挑战，主要由于RES发电的固有变异性。准确预测RES发电量对于维持电力系统运行的可靠性、稳定性和经济效率至关重要。传统方法，如确定性方法和随机规划，经常依赖于通过聚类技术如K均值生成的代表性场景。然而，这些方法可能无法充分捕捉RES数据中的复杂时间依赖性和非线性模式。本文介绍了一种多变量长短期记忆（LSTM）网络，用于利用其真实历史数据预测RES发电量。所提出的模型有效捕捉了长期依赖性和不同RES之间的相互作用，利用本地和邻近地区的 historical 数据来提高预测准确性。在案例研究中，我们展示了所提出的预测方法可以降低二氧化碳排放，并提高电力负载的供应可靠性。
英文摘要：
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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