This paper proposes a multi-data driven hybrid learning method for weekly photovoltaic (PV) power scenario forecast that is coordinately driven by weather forecasts and historical PV power output data. Patterns of historical data and weather forecast information are simultaneously captured to ensure the quality of the generated scenarios. By combining bicubic interpolation and bidirectional long-short term memory (BiLSTM), a super resolution algorithm is first presented to enhance the time resolution of weather forecast data from three hours to one hour and increase the precision of weather forecasting. A weather process-based weekly PV power classification strategy is proposed to capture the coupling relationships between meteorological elements, continuous weather changes and weekly PV power. A gated recurrent unit (GRU)-convolutional neural network (CNN)-based scenario forecast method is developed to generate weekly PV power scenarios. Evaluation indices are presented to comprehensively assess the quality of the generated weekly scenarios of PV power. Finally, the PV power, weather observation and weather forecast data collected from five PV plants located in Northeast Asia are used to verify the effectiveness and correctness of the proposed method.

中文翻译：

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一种多数据驱动的混合学习方法，用于每周光伏发电场景预测

本文提出了一种多数据驱动的混合学习方法，用于由天气预报和历史光伏功率输出数据协调驱动的每周光伏（PV）电力场景预测。同时捕获历史数据和天气预报信息的模式，以确保生成场景的质量。通过结合双三次插值和双向长短期记忆（BiLSTM），首次提出了一种超分辨率算法，将天气预报数据的时间分辨率从三小时提高到一小时，提高了天气预报的精度。提出了一种基于天气过程的周光伏功率分类策略，以捕捉气象要素、连续天气变化和周光伏功率之间的耦合关系。开发了一种基于门控循环单元 (GRU)-卷积神经网络 (CNN) 的情景预测方法来生成每周光伏发电情景。提出评价指标，综合评价每周生成的光伏发电场景质量。最后，利用东北亚5家光伏电站采集的光伏发电、天气观测和天气预报数据，验证了所提方法的有效性和正确性。