Photovoltaic ultra-short-term prediction method and system

Abstract

The invention provides a photovoltaic ultra-short-term prediction method, which comprises the following steps: carrying out pixel standardization processing on a satellite visible light cloud picture,and carrying out bottom removal operation on the standardized cloud picture to obtain an image only containing cloud information; predicting the cloud picture of the target area at the ultra-short time in the future by using the image only containing the cloud information; inputting the prediction cloud picture into a convolutional neural network to obtain a cloud occlusion factor; and fusing thecloud shielding factor and the influence factor related to the photovoltaic power or irradiance, establishing a mapping relationship with the photovoltaic power or irradiance, and predicting the to-be-predicted quantity by utilizing the relationship. A satellite visible light cloud picture data acquisition way is simple and wide, and meanwhile, the coverage area and the visual field are large, and the cloud layer movement and thickness conditions can be reflected. The convolutional neural network is especially suitable for extraction and learning of image features, and the shielding influenceof cloud on radiation light can be better captured by fusing the convolutional neural network with a satellite cloud picture.

Description

technical field [0001] The disclosure belongs to the field of power grid new energy forecasting, and in particular relates to a photovoltaic ultra-short-term forecasting method and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Global warming and energy crisis promote the use and development of sustainable and clean energy. Solar energy is considered to be one of the most promising renewable energy sources, and the development of new energy sources represented by photovoltaics has become the consensus of all countries in the world. However, as an intermittent energy source, photovoltaic power generation brings huge fluctuations to the smart grid, and poses severe challenges to system stability, power balance, reactive power compensation, and frequency response. To ensure the safe and economical integration of photovoltaics into...

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Problems solved by technology

The model of the above method is simple and the calculation speed is fast, but it is impossible to deeply dig out the factors that affect the change of photovoltaic power or irradiance, and cannot guarantee high accuracy

[0006] In recent years, artificial intelligence methods have begun to be used in the field of new energy forecasting, such as artificial neural networks, random forests and other methods are gradually integrated into photovoltaic forecasting technology, but traditional artificial intelligence methods rely on a large amount of historical data, image feature extraction and depth Not good at learning

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