Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study

Chen, Ji‐Long; Liu, Hongbin; Wu, Wei; Xie, Deti

doi:10.1016/j.renene.2010.06.024

Cited by 174 publications

(73 citation statements)

References 20 publications

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“…Results indicated that SVM was superior than the MLP model based on the error analysis between the actual and the predicted data. Chen, et al [6] studied the feasibility of SVMs in predicting monthly solar radiation with air temperatures based on the statistical learning theory. Comparison showed that the newly developed SVM model performed better than other SVM models and empirical methods.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Temperature Fields induced by Natural Convention in a Cylindrical Enclosure using Fuzzy LS-SVM

Zhou¹,

Cao²,

Du³

et al. 2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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Abstract-Support Vector Machines (SVM) is a machine learning algorithm basing on the statistical learning theory. In this study, SVM is used for the prediction of temperature field induced by natural convection in a cylindrical enclosure. Because of the large amount of computing and poor real-time characteristics of conventional SVM, and also to solve the non optimal problem in the whole situation and the over-fitting phenomenon, Fuzzy LS-SVM is adopted. The heat transfer in the enclosure is an unsteady process. The heat transfer process is firstly simulated with CFD software, then part of simulated data is picked for training of LS-SVM model and the rest of data is used for validation of the model. The prediction results are successfully validated from the mean relative error (MRE), max relative error (MAE), mean square error (MSE) and absolute fraction of variance (R 2 ). Besides, by comparison with artificial neural network based on back propagation (BP-ANN), the fuzzy LS-SVM shows more superior performance in both prediction accuracy and computation efficiency.

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Section: Introductionmentioning

confidence: 99%

Prediction of Temperature Fields induced by Natural Convention in a Cylindrical Enclosure using Fuzzy LS-SVM

Zhou¹,

Cao²,

Du³

et al. 2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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show abstract

“…In order to meet the requirements of solar radiation data for different studies, several methods are used to estimate solar radiation including mechanism model [3], satellite-derived method [4], stochastic algorithm [5], empirical model [6] and learning machine method [7]. Among these methods, the empirical model using observed meteorological data is attractive for the good performances and data availability [8].…”

Section: Introductionmentioning

confidence: 99%

Mapping Solar Radiation at Different Timescales Using Both Sunshine Duration and Diurnal Temperature Range in China

Zeng¹,

Du²,

Wan³

et al. 2017

Topics in Civil, Environmental &Amp; Building Engineering (TICEBE)

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Previous studies investigating on the spatiotemporal variation of solar radiation are mainly focus on the annual changes, while higher time resolution spatiotemporal distributions are limited, which will be more important for different biogeochemical and hydrological processes with different timescales. In this paper, the spatiotemporal distribution of solar radiation at annual and seasonal timescales are mapped in China based on three widely used models including the Ångström-Prescott, Hargreaves-Samani and Bristow-Campbell models. Results show that the annual solar radiation decreases significantly in the Eastern monsoon zone and the Northwestern arid zone. The seasonal solar radiation decreases in most of the season in the Northwestern arid zone and the decrease trends are significantly in spring and autumn in the Eastern monsoon zone, while the changing trends are not significant in most of the seasons in the Tibetan Plateau frigid zone. Moreover, "From diming to brightening" occurs in winter, annual and particularly in spring radiation changes, while in summer solar radiation shows a continuous decrease trend and a slight decrease trend is detected in autumn solar radiation.

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“…Statistical methods utilize the information of historical data and analyze the inherent law of historical data to forecast the PV output [7]- [12]. The commonly used statistical forecast methods are time series method [7], artificial neural network (ANN) method [8] and support vector machine (SVM) [9]. In [11], prediction models for solar power generation are built.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic power forecasting with a rough set combination method

Yang

Ren

Yue

2016

2016 UKACC 11th International Conference on Control (CONTROL)

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Abstract-One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.

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Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study

Cited by 174 publications

References 20 publications

Prediction of Temperature Fields induced by Natural Convention in a Cylindrical Enclosure using Fuzzy LS-SVM

Prediction of Temperature Fields induced by Natural Convention in a Cylindrical Enclosure using Fuzzy LS-SVM

Mapping Solar Radiation at Different Timescales Using Both Sunshine Duration and Diurnal Temperature Range in China

Photovoltaic power forecasting with a rough set combination method

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