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A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data

机译:LSTM神经网络通过卫星数据预测日前全球水平辐照度的比较研究

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Accurate forecasts of solar energy are important for photovoltaic (PV) based energy plants to facilitate an early participation in energy auction markets and efficient resource planning. The study concentrates on Long Short Term Memory (LSTM), a novel forecasting method from the family of deep neural networks, and compares its forecasting accuracy to alternative methods with a proven track record in solar energy forecasting. To provide a comprehensive and reliable assessment of LSTM, the study employs remote-sensing data for testing predictive accuracy at 21 locations, 16 of which are in mainland Europe and 5 in the US. To that end, a novel framework to conduct empirical forecasting comparisons is introduced, which includes the generation of virtual PV plants. The framework enables richer comparisons with higher coverage of geographical regions. Empirical results suggest that LSTM outperforms a large number of alternative methods with substantial margin and an average forecast skill of 52.2% over the persistence model. An implication for energy management practice is that LSTM is a promising technique, which deserves a place in forecasters' toolbox. From an academic point of view, LSTM and the proposed framework for experimental design provide a valuable environment for future studies that assess new forecasting technology.
机译:准确的太阳能预报对于基于光伏(PV)的能源工厂至关重要,以促进及早参与能源拍卖市场和有效的资源规划。该研究集中于长期短期记忆(LSTM),这是一种来自深度神经网络的新型预测方法,并将其预测准确性与在太阳能预测中具有良好记录的替代方法进行了比较。为了提供对LSTM的全面而可靠的评估,该研究使用遥感数据在21个位置(其中16个在欧洲大陆和5个在美国)测试预测准确性。为此,引入了进行经验预测比较的新颖框架,其中包括虚拟光伏电站的产生。该框架可实现更丰富的比较,并覆盖更大的地理区域。实验结果表明,与持久性模型相比,LSTM的性能优于大量替代方法,且具有可观的余量,平均预测技能为52.2%。能源管理实践的一个含义是,LSTM是一种很有前途的技术,应在预测人员的工具箱中占有一席之地。从学术角度来看,LSTM和拟议的实验设计框架为评估新的预测技术的未来研究提供了宝贵的环境。

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