Photovoltaic power prediction method based on a convolutional neural network and meta-learning

Abstract

The invention discloses a photovoltaic power prediction method based on a convolutional neural network and meta-learning. The method belongs to the technical field of renewable energy development andutilization, and comprises the steps of establishing a deep convolutional neural network model, taking historical photovoltaic power data, historical meteorological data and numerical meteorological forecast data as model input, and taking photovoltaic power of a day to be predicted as output to form a model training sample; analyzing the weather type of the day to be predicted based on the radiation data in the numerical weather forecast, and selecting a similar day training sample; based on a meta-learning strategy and similar day training samples, training a neural network model by using eight loss function indexes, and outputting eight prediction results to realize point prediction and probability prediction of photovoltaic power. The invention also discloses a prediction system of themethod. The method can adapt to photovoltaic power prediction conditions of different seasons and different weathers, has extremely high prediction precision, and can effectively improve the operation stability of the photovoltaic grid-connected system.

Description

technical field [0001] The invention belongs to the technical field of renewable energy development and utilization, and in particular relates to a photovoltaic power prediction method and system based on convolutional neural network and meta-learning. Background technique [0002] As a widely distributed and easy-to-obtain renewable resource, solar energy can effectively deal with the current world's resource shortage and environmental pollution problems, so it has been fully utilized and developed in photovoltaic power generation and other fields. However, the output of photovoltaic power generation is affected by the weather and environment, and its output power fluctuates greatly, which has a certain impact on the photovoltaic grid-connected power system and threatens its safe and stable operation. Therefore, photovoltaic power prediction technology has received extensive attention. Among them, the day-ahead photovoltaic power prediction can provide information guidance...

AI Technical Summary

Problems solved by technology

The statistical method only inputs the historical photovoltaic power sequence, so it is more suitable for hourly forecasting, and the accuracy is poor in day-ahead forecasting; the satellite data method, that is, realizes photovoltaic power forecasting by analyzing satellite cloud images and satellite radiation data, but due to its high cost , there ar

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