Solar radiation prediction using different techniques: model evaluation and comparison

Wang, Lunche; Kisi, Özgür; Zounemat‐Kermani, Mohammad; Salazar, Germán Ariel; Zhu, Zhenfeng; Gong, Wei

doi:10.1016/j.rser.2016.04.024

Cited by 258 publications

(97 citation statements)

References 87 publications

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“…The first tendency reflects the problems of underestimation for high values of CO 2 flux in training and testing data sets, which is general in statistical prediction models [24,45]. Because the inputs cannot totally explain the outputs especially for extreme values, this tendency can also partly attribute to the non-homogeneous nature of data.…”

Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

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Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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“…Many approaches have been developed to estimate surface solar radiation accurately (Khatib et al, ; Liu et al, ; Olatomiwa et al, ; Sun et al, ; Wang et al, ; Zhang et al, ), which can be generally categorized as follows: Estimation by semiempirical and semiphysical formulae with conventional meteorological parameters (e.g., sunshine hour, cloud, temperature, and humidity) as inputs (Angstrom, ; Davies et al, ; Thornton & Running, ; Bakirci, ; Besharat et al, ; Hassan et al, ; Liu et al, ). A simple empirical model can be established with its coefficients varying with time and space, which need to be calibrated using long‐term radiation observation data in certain areas. Estimation by the artificial neural network method with meteorological observations (Jiang, ; Linares‐Rodriguez et al, ; Tang et al, ; Ramedani et al, ; Kashyap et al, ; Wang et al, ). The artificial neural network method requires a large number of samples to train the model in a local area, and the trained model may not be applicable in other areas. Retrieval by satellite‐based radiation (Ceballos, ; Liang et al, ; Mueller et al, ; Lu et al, ; Huang et al, ; Qin et al, ; Ma & Pinker, ; Jia et al, ; Zhang et al, ).…”

Section: Discussionmentioning

confidence: 99%

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Zeng

Wang

Gui

et al. 2020

Earth and Space Science

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Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

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“…MLPs are organized as hierarchical networks with several layers including an input layer, hidden layer (s) and an output layer (Wang et al, 2016). There are one or more hidden layers between the input and output layers which are connected by neurons (including synaptic weights, biases and activation or transfer functions).…”

Section: Methodsmentioning

confidence: 99%

Pan evaporation modeling using six different heuristic computing methods in different climates of China

Wang

Kisi

Zounemat‐Kermani

et al. 2017

Journal of Hydrology

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106

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Solar radiation prediction using different techniques: model evaluation and comparison

Cited by 258 publications

References 87 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Pan evaporation modeling using six different heuristic computing methods in different climates of China

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