Method and device for GRU-based medium and long-term prediction of irradiance of photovoltaic power station

Abstract

The invention discloses a method and a device for GRU-based medium and long-term prediction of irradiance of a photovoltaic power station, relates to methods and devices for medium and long-term prediction of the irradiance of the photovoltaic power station and belongs to the field of irradiance prediction. The method comprises the steps that solar irradiance data at the position of the photovoltaic power station is collected; the solar irradiance at the position of the photovoltaic power station is predicted based on a GRU (threshold recursion unit) neural network model; an electric power department performs medium and long-term planning and scheduling according to an obtained irradiance prediction result, reasonable utilization of resources is realized, and equipment loss and other problems caused by unreasonable increase of the installed capacity are reduced. The invention furthermore discloses the device for implementing the method. The device comprises a collection module, a prediction module and a decision making module. Based on the GRU (threshold recursion unit) neural network model, a memory function is achieved, and medium and long-term prediction of the irradiance of the photovoltaic power station can be realized. Besides, the method and the device also have the advantages of high accuracy, high efficiency and simple implementation.

Description

technical field [0001] The invention belongs to the technical field of irradiance prediction, and in particular relates to a method and device for mid- and long-term prediction of irradiance of a photovoltaic power station based on a GRU (gated recurrent unit, threshold recursive unit) neural network model. Background technique [0002] Due to the increasing popularity of photovoltaic power plants, solar irradiance prediction is becoming increasingly important. To ensure the stable operation of power systems, accurate irradiance prediction is crucial. [0003] However, most of the existing irradiance predictions are short-term predictions, which cannot provide guidance for medium and long-term planning and dispatching of power systems. Moreover, most of the current prediction methods need to collect meteorological data related to irradiance, such as temperature, humidity and cloud cover, etc. Errors in the process of collecting these additional data will have a certain impa...

AI Technical Summary

Problems solved by technology

[0003] However, most of the existing irradiance predictions are short-term predictions, which cannot provide guidance for medium and long-term planning and scheduling of power systems.

Moreover, most of the current prediction methods need to collect meteorological data related to irradiance, such as temperature, humidity and cloud cover, etc. Errors in the process of collecting these additional data will have a certain impact on subsequent irradiance predictions

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