Photovoltaic power generation power control method based on gating convolution and attention mechanism

Abstract

The invention discloses a photovoltaic power generation power control method based on gating convolution and an attention mechanism. The method comprises the steps that an end-to-end deep learning model BiGCNN composed of a Conv + Pool layer stacked network layer, a BiGLU network layer and an Attention network layer is constructed; wherein the BiGLU network layer and the Attention network layer are respectively used for extracting short-term and long-term time sequence dependence relations; weather data collected by sensors and meteorological satellites installed near photovoltaic equipment toa computer is transmitted through a wireless network; the computer loads the deep learning model BiGCNN to predict the power generation power at a certain moment in the future according to the numerical weather data in a past period of time and the historical power generation power of the photovoltaic equipment; based on the predicted power generation power, a photovoltaic power generation and planning system is helped to take positive defensive measures. By controlling the prediction result, the photovoltaic power generation and planning system can be helped to take positive defense measures.

Description

technical field [0001] The present invention relates to the field of time series data control of machine learning, in particular to a control method of photovoltaic power generation based on gated convolution and global attention mechanism, through the analysis of weather data collected in the past period of time and historical power generation And use it to predict the photovoltaic power generation power at a certain time in the future, and then realize the effective control of photovoltaic power generation power. Background technique [0002] Power forecasting for renewable energy power plants has been a very active research area in recent years. Forecasting electricity generation for some time in the future ensures safe operation of the grid and helps minimize the operating costs of renewable energy. Solar energy is one of the best renewable clean energy to replace traditional energy, and photovoltaic power generation has become a very important topic. The main influenc...

AI Technical Summary

Problems solved by technology

Although RNNs can effectively extract time series features, there are the following problems: 1. The sequence data is calculated moment by moment. The calculation at the current moment depends on the previous results and cannot be accelerated in parallel; 2. It can only use the information at the previous moment

But in practice, the gradient is often dominated by the closer moments, making it difficult for the model to model long-distance dependencies in the sequence

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