Clean energy generation power prediction method and system

Abstract

The invention relates to the technical field of clean energy utilization, and discloses a clean energy generation power prediction method and system. The clean energy power generation power prediction method comprises the following steps: S1, carrying out real-time monitoring and data acquisition on working condition data of clean energy power generation equipment, and monitoring and acquiring power output data of the power generation equipment in real time through an electric energy meter installed in the clean energy power generation equipment; and S2, constructing a high-quality data sample from a historical monitoring data sequence and applying the high-quality data sample to training a power output machine learning model. According to the clean energy generation power prediction method and system, high-quality training sample extraction and abnormal data sample interference elimination can be carried out, corresponding prediction means can be adopted according to working condition characteristics of different conditions, and prediction precision is improved.

Description

technical field [0001] The invention relates to the technical field of clean energy utilization, in particular to a method and system for predicting power generation of clean energy. Background technique [0002] As energy security and environmental protection have become the focus of attention in the world today, my country, as a major energy consumer, clearly puts forward the strategic goals of "carbon peak" by 2030 and "carbon neutrality" by 2060. [0003] In the energy network, a comprehensive energy system is constructed by integrating clean energy such as photovoltaics and wind power. Realize the mutual coupling of various energy flows of photovoltaic, wind power, and coal power, give full play to the advantages of different energy systems, coordinate operation, improve energy utilization efficiency and clean energy allocation and consumption, and realize energy security and environmental protection. [0004] The power output of clean energy such as photovoltaics and ...

AI Technical Summary

Problems solved by technology

These methods have achieved good results to a certain extent, but the artificial neural network has the disadvantage of requiring high quality training samples; the support vector machine has the problem of difficult selection of penalty function, kernel function and kernel parameters.

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