Global solar radiation prediction model with random forest algorithm

Kör, Hakan

doi:10.2298/tsci200608004k

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…In the application, data are randomly extracted from the dataset D to form a new training set, the training set is subjected to classification regression, and the final output is obtained after weighted calculation and arithmetic average calculation [18]. The dataset of the decision tree can be effectively enhanced by this step, and the generalization ability is improved.…”

Section: Skill Evaluation Model Based On Stochastic Algorithmmentioning

confidence: 99%

“…When N takes an infinite value and P takes a value of 0.368, 37% of the data in the training set will not be drawn, and this part of the sample is represented by OOB, a metric that can be used to analyze the error and can also respond to the importance [18]. Based on the submodel of the randomized algorithm when there is the CART model, the Gini index is used as a splitting metric when the decision tree nodes are split to generate a bifurcated decision tree, and each division divides the sample into two subsets.…”

Section: Skill Evaluation Model Based On Stochastic Algorithmmentioning

confidence: 99%

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Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group

Jia

2022

Advances in Multimedia

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At present, the employment situation in China is very severe, and there are both recruitment and employment difficulties in the society. There are many reasons for this problem, among which, the incompatibility between talents training and social demand in higher vocational colleges is one of the main reasons, and it is necessary for higher vocational colleges to effectively evaluate students’ skills training with the real demand. Based on this, this paper studies the study of stochastic algorithm and skill evaluation system based on the combing of the construction mechanism of higher vocational professional clusters and builds a higher vocational skill evaluation system based on a simple analysis of the skill evaluation system and related evaluation algorithms in higher vocational institutions. Principal component analysis was selected to realize the quantitative processing of skill evaluation indexes, and random forest algorithm was used to realize skill evaluation. The simulated annealing algorithm is introduced to realize parameter selection, parameter optimization, and weight setting, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the random forest algorithm is applied to skill evaluation with high accuracy, small error, and better generalization ability.

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Section: Skill Evaluation Model Based On Stochastic Algorithmmentioning

confidence: 99%

Section: Skill Evaluation Model Based On Stochastic Algorithmmentioning

confidence: 99%

Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group

Jia

2022

Advances in Multimedia

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“…The regional estimation of solar radiation has shifted from measurements using expensive and highly maintained instruments to model calculations ( 13 , 14 ). Various empirical models, for example, sunshine-based models ( 15 – 18 ) and temperature-based models ( 19 – 21 ) are commonly used due to their simple operation and low computational costs ( 22 – 26 ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Daily Ground-Received Global Solar Radiation Using Air Pollutant Data

Zhang

Zhang²,

Cui

et al. 2022

Front. Public Health

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Ground-received solar radiation is affected by several meteorological and air pollution factors. Previous studies have mainly focused on the effects of meteorological factors on solar radiation, but research on the influence of air pollutants is limited. Therefore, this study aimed to analyse the effects of air pollution characteristics on solar radiation. Meteorological data, air quality index (AQI) data, and data on the concentrations of six air pollutants (O3, CO, SO2, PM10, PM2.5, and NO2) in nine cities in China were considered for analysis. A city model (model-C) based on the data of each city and a unified model (model-U) based on national data were established, and the key pollutants under these conditions were identified. Correlation analysis was performed between each pollutant and the daily global solar radiation. The correlation between O3 and daily global solar radiation was the highest (r = 0.575), while that between SO2 and daily global solar radiation was the lowest. Further, AQI and solar radiation were negatively correlated, while some pollution components (e.g., O3) were positively correlated with the daily global solar radiation. Different key pollutants affected the solar radiation in each city. In Shenyang and Guangzhou, the driving effect of particles on the daily global solar radiation was stronger than that of pollutants. However, there were no key pollutants that affect solar radiation in Shanghai. Furthermore, the prediction performance of model-U was not as good as that of model-C. The model-U showed a good performance for Urumqi (R2 = 0.803), while the difference between the two models was not particularly significant in other areas. This study provides significant insights to improve the accuracy of regional solar radiation prediction and fill the gap regarding the absence of long-term solar radiation monitoring data in some areas.

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“…Karasu and Altan proposed a high-performance recognition model for solar radiation based on RF with a feature selection approach to cope with nonlinear dynamics in time series. Kör utilized the RF algorithm to estimate global solar radiation, and results showed that the RF model performed well with a predictive value of 93.9% and determination coefficient ( R 2 ) above 0.938. Generally, RF is effective for solving multivariate nonlinear problems and can be used for downscaling large-scale atmospheric variables and predicting SRI; however, the RF method has difficulties in quantifying the main and interactive effects of climate variables on the SRI …”

Section: Introductionmentioning

confidence: 99%

Assessing Distributed Solar Power Generation Potential under Multi-GCMs: A Factorial-Analysis-Based Random Forest Method

Zhou

Huang

et al. 2022

ACS Sustainable Chem. Eng.

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The development of renewable energy is important for climate change mitigation and socioeconomic sustainability, and the prediction of renewable energy potential (e.g., solar) under the consideration of climate change impact is challenged. In this study, a factorial-analysis-based random forest (FARF) method is developed for the distributed solar power generation (DSPG) predication under multiple global climate models (GCMs). FARF has advantages in (i) downscaling large-scale climate variables to local scales, (ii) avoiding the problem of overfitting in traditional models; and (iii) reflecting the main and interactive effects of climate variables on solar radiation intensity (SRI). Then, the FARF method is applied to the Jing-Jin-Ji region of China to predict the DSPG potential under three GCMs and two emission scenarios (RCP4.5 and 8.5). Multiple validation coefficients prove that the FARF method is effective and feasible. Major findings are as follows: (i) during 2021−2100, the regional SRI would increase under all GCMs and RCPs, and the southern region is obviously higher than the northern region; (ii) the main impact factors are temperature (contribution >51%) and humidity (contribution >28%), and the interactive effects of multiple factors are insignificant; (iii) the regional DSGP would continuously rise and its contribution to electricity consumption would continue to increase; and (iv) under all GCMs, SRI and DSPG under RCP8.5 would be higher than those under RCP4.5. The findings can help decision makers to use the desired strategies for promoting renewable energy utilization and energy system sustainable development.

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Global solar radiation prediction model with random forest algorithm

Cited by 7 publications

References 24 publications

Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group

Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group

Estimation of Daily Ground-Received Global Solar Radiation Using Air Pollutant Data

Assessing Distributed Solar Power Generation Potential under Multi-GCMs: A Factorial-Analysis-Based Random Forest Method

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