用LSTM预测太阳能辐照度，帮光伏电站减少日内市场罚款。
Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation
- 用LSTM和Bi-LSTM模型，10分钟分辨率预测6小时内的太阳辐照度。
- LSTM训练时间仅6小时，性能接近Bi-LSTM且误差指标达标。
- 模型可生成小时级预报，适合需要精准报价的日内电力市场参与者。
太阳能辐照度预测能提升光伏电站发电可靠性及电网并网稳定性。在哥伦比亚，若光伏电站实际发电量超出政府设定的日内市场报价阈值，将面临处罚。本研究采用长短期记忆（LSTM）与双向长短期记忆（Bi-LSTM）模型，基于哥伦比亚塞萨尔省埃尔普埃霍地区某光伏电站的气象数据，进行6小时预测时长、10分钟时间分辨率的太阳辐照度预测。尽管Bi-LSTM表现更优，但LSTM在训练时间上显著更短（6小时对18小时），具备更强计算优势。将LSTM预测结果平均后生成小时级模型，使用平均绝对误差（MAE）、均方根误差（RMSE）、归一化均方根误差（NRMSE）和平均绝对百分比误差（MAPE）进行评估。与全球预报系统（GFS）相比，两者性能相近，均能有效捕捉日间辐照度变化规律。该预测模型已集成至面向对象的功率生产模型中，可实现日内市场的精准能源报价，从而降低惩罚成本。
原文摘要 · Abstract (English)
The prediction of solar irradiance enhances reliability in photovoltaic (PV) solar plant generation and grid integration. In Colombia, PV plants face penalties if energy production deviates beyond governmental thresholds from intraday market offers. This research employs Long Short-Term Memory (LSTM) and Bidirectional-LSTM (Bi-LSTM) models, utilizing meteorological data from a PV plant in El Paso, Cesar, Colombia, to predict solar irradiance with a 6-hour horizon and 10-minute resolution. While Bi-LSTM showed superior performance, the LSTM model achieved comparable results with significantly reduced training time (6 hours versus 18 hours), making it computationally advantageous. The LSTM predictions were averaged to create an hourly resolution model, evaluated using Mean Absolute Error, Root-Mean-Square Error, Normalized Root-Mean-Square Error, and Mean Absolute Percentage Error metrics. Comparison with the Global Forecast System (GFS) revealed similar performance, with both models effectively capturing daily solar irradiance patterns. The forecast model integrates with an Object-Oriented power production model, enabling accurate energy offers in the intraday market while minimizing penalty costs.
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