Solar collector output probability modeling method based on nonparametric kernel density estimation

Abstract

The invention discloses a solar collector output probability modeling method based on non-parametric kernel density estimation. The method comprises the following steps of: 1. obtaining hourly meteorological data measured in history and counting according to different years; 2. solving the output value and the bandwidth value of the unit area collector through a non-parametric kernel density estimation model of the output of the solar collector; 3. modifying the nonparametric kernel density estimation model. The method of the present invention can accurately reflect the random characteristicsand variation law of the output of the collector under different time scales, and can be used for planning and reliability research of the photothermal system under the condition of lacking historicaloperation data in the early stage.

Description

technical field [0001] The invention relates to the technical field of photothermal system modeling, in particular to a method for modeling the output probability of solar heat collectors. Background technique [0002] Today, with the increasingly prominent energy problems, solar energy, as a clean and widely sourced renewable energy, has attracted widespread attention. Solar thermal collectors can effectively convert solar energy into thermal energy, but their heating capacity is affected by factors such as outdoor temperature and solar radiation, resulting in random, intermittent, and periodic output. However, the current research on the output of solar thermal collectors is concentrated In the performance analysis of its mechanism model, the uncertainty of the actual collector output due to the randomness of the actual weather conditions and the differences of different collector models is not considered, so the output of the solar collector cannot be accurately evaluated...

AI Technical Summary

Problems solved by technology

[0005] Both of the above two probabilistic modeling methods for wind power have shortcomings. The first technical solution only considers the fitting when considering the constrained bandwidth optimization model The goodness test does not correct the results after the actual data analysis, so the error between the probability model and the observed distribution cannot be accurately evaluated, and the quantitative analysis of the error is lacking, which may lead to a large error

The second technical solution does not take into account the different probability density functions of wind farms in different regions at different time scales, resulting in different final probability distribution results. It only considers the historical data of a single wind farm within the preset time range, and lacks simulation Goodness test and actual correction, no result evaluation index

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