基于LSTM的风光互补微电网净负荷预测
LSTM-Based Net Load Forecasting for Wind and Solar Power-Equipped Microgrids
AI总结：
本文提出基于LSTM的深度学习模型预测含风光发电的微电网净负荷，通过TMY住宅微电网案例验证其有效性，为可再生能源微电网能源管理提供支持。
摘要：
随着太阳能和风能等可变可再生能源（RES）的并网比例不断提高，电网运行和能源管理中引入了显著的不确定性。有效的预测模型对于电网运营商预测净负荷——即用户电力需求与可再生能源发电量之间的差值——至关重要。本文提出了一种基于长短期记忆（LSTM）网络的深度学习（DL）模型，用于可再生能源微电网中的净负荷预测，同时考虑太阳能和风能。文中详细介绍了该模型的架构，并使用基于典型气象年（TMY）数据集的住宅微电网测试案例对其性能进行了评估。结果表明，所提出的基于LSTM的深度学习模型在预测净负荷方面具有有效性，展示了其在增强可再生能源微电网能源管理方面的潜力。
英文摘要：
The rising integration of variable renewable energy sources (RES), like solar and wind power, introduces considerable uncertainty in grid operations and energy management. Effective forecasting models are essential for grid operators to anticipate the net load - the difference between consumer electrical demand and renewable power generation. This paper proposes a deep learning (DL) model based on long short-term memory (LSTM) networks for net load forecasting in renewable-based microgrids, considering both solar and wind power. The model's architecture is detailed, and its performance is evaluated using a residential microgrid test case based on a typical meteorological year (TMY) dataset. The results demonstrate the effectiveness of the proposed LSTM-based DL model in predicting the net load, showcasing its potential for enhancing energy management in renewable-based microgrids.
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