Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter

a b s t r a c tThis paper tackles the prediction of the global solar radiation (GSR) at a given point, using as predictive variables the outputs of a numerical weather model (the WRF meso-scale model) obtained at a different grid points. Prediction is obtained in this work using a Multilayer Perceptron (MLP) trained with Extreme Learning Machines (ELMs). Provided that the number of WRF outputs is vast, we propose the use of a Coral Reefs Optimization algorithm with species (CRO-SP) to obtain a reduced number of significant predictive variables, therefore improving the global solar radiation prediction attained without feature selection. The proposed system has been tested on real data from a radiometric station located at Toledo (Spain) and average best results of RMSE of 69.19 W=m 2 have been achieved, resulting in a 21.62% improvement over the average prediction without considering the CRO-SP for the feature selection.

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“…In Ref. [22] a comparison between the performance of SVR and ANNs is carried out, in a problem of photovoltaic power generation. The work in Ref.…”

Section: Introductionmentioning

confidence: 99%

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

et al. 2017

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“…The method combined a firefly meta-heuristic algorithm with support vector regression (SVR) and was designed by Olatomiwa et al [9] to evaluate the accuracy of developed methods for solar radiation estimation in Nigeria. Antonanzas et al [20] evaluated SVR performance for mapping solar irradiation using exogenous input variables, whereas Monteiro et al [21] compared the ability of two models (e.g., ANNs and SVR) to generate photovoltaic power. Chen et al [22] estimated a solar radiation problem using least-square SVR (LSSVR) based on the atmospheric data at Chongqing meteorological station, China.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Rezaie-Balf

Maleki²,

Kim

et al. 2019

Energies

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The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

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“…In recent years, ANN has become used widely for forecasting in various fields of research including finance, power generation, pharmaceutical, water and environmental resources (Li et al ., 2014, 2015; Qiu et al ., 2016; Velásco-Mejía et al ., 2016; Monteiro et al ., 2017). In agriculture, ANN is one of the main machine learning models which have been used widely (van Evert et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

Bagheri

Eghbali²,

Haghighi³

2019

J. Agric. Sci.

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The current study was conducted in 2013 to identify the seeds of three species of Amaranthus, Amaranthus viridis L., Amaranthus retroflexus L. and Amaranthus albus L., by using the artificial neural network (ANN) and canonical discriminant analysis (CDA) methods. To begin with, photographs were taken of the seeds and 13 morphological characteristics of each seed extracted as predictor variables. Backward regression was used to find the most influential variables and seven variables were derived. Thus, predictor variables were divided into two sets of 13 and seven morphological characteristics. The results showed that the recognition accuracy of the ANN made using 13 and seven predictor variables was 81.1 and 80.3%, respectively. Meanwhile, recognition accuracy of the CDA using the seven and 13 predictor variables was 74.0 and 75.7%, respectively. Therefore, in comparison to CDA, ANN showed higher identification accuracy; however, the difference was not statistically significant. Identification accuracy for A. retroflexus was higher using the CDA method than ANN, while the ANN method had higher recognition accuracy for A. viridis than CDA. In addition, use of 13 predictor variables yielded a greater identification accuracy than seven. The results of the current study showed that using seed morphological characteristics extracted by computer vision could be effective for reliable identification of the similar seeds of Amaranthus species.

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Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter

Cited by 47 publications

References 40 publications

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

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