Based on existing power generation data, an hourly area solar power estimation model using the parallel Elman neural network with solar radiation and system conversion efficiency is proposed. The accuracy and reliability of the assessment were verified using the information/data of solar photovoltaic power stations in various regions and timescales. Using the established appraisal algorithm involving K‐means evaluation and inverse distance weighting, regional forecasting of solar power generation was achieved. The prediction accuracy was also investigated using the actual details of the photovoltaic power stations. The results of the proposed model can assist the electricity dispatcher to not only precisely monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet the load demand more accurately. The proposed method can benefit power dispatching involving a larger scale of intermittent and unstable solar power electricity in the future.

中文翻译：

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并行Elman神经网络在时区太阳能光伏电站发电量估算中的应用

基于现有的发电数据，提出了利用太阳辐射和系统转换效率的并行埃尔曼神经网络的小时面积太阳能估计模型。评估的准确性和可靠性使用了各个地区和时间范围内的太阳能光伏电站的信息/数据进行了验证。使用建立的评估算法，包括K均值评估和距离反比加权，可以实现太阳能发电的区域预测。还使用光伏电站的实际细节研究了预测精度。该模型的结果不仅可以帮助电力调度员精确监控不同地区太阳能发电的趋势，而且还可以与传统发电厂进行协调，以更准确地满足负载需求。所提出的方法将有利于将来涉及较大规模的间歇性和不稳定太阳能电力的电力调度。