Daily solar irradiation estimation over a mountainous area using artificial neural networks

Bosch, J.L.; López, Gabriel; Batlles, F.J.

doi:10.1016/j.renene.2007.09.012

Cited by 110 publications

(48 citation statements)

References 24 publications

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“…Bosch et al (2008) performed an investigation to estimate daily global solar radiation using ANN over a mountainous region situated in South East of Spain. Their results demonstrated that ANN can be considered as an effective technique with easy usage for estimation of solar radiation in complex mountain terrains.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Gani

Mohammadi

Shamshirband

et al. 2015

Theor Appl Climatol

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The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neurofuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m 2 , 0.50-0.71 kWh/m 2 , and 0.78-0.91, respectively.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Gani

Mohammadi

Shamshirband

et al. 2015

Theor Appl Climatol

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show abstract

“…Also, the model achieved a maximum value of the correlation coefficient (R) equal to 0.97 and a minimum value equal to 0.67. Bosch et al [25] developed an ANN model to determine the daily global irradiation at stations located in a complex terrain. The collected data sets consisted of 3 years' data of daily global radiation from 12 different stations located in the north face of the Sierra Nevada National Park in the South East of Spain.…”

Section: Related Artificial Neural Network-based Workmentioning

confidence: 99%

Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

2017

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Abstract:The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R 2 ). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R 2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

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“…There are a few studies in this area, such as J.L. Bosch et al (2008), who performed a study in which the performance of active and passive energy systems are analysed and J.C. Winslow et al (2001), who developed a model of daily solar irradiance estimated from air temperature and precipitation data. In this last approach, the best results were obtained through extrapolation/interpolation of the acquired data in a station near of the study location, but even there it was difficult to take problems such as terrain complexity, into account.…”

Section: Solar Radiationmentioning

confidence: 99%

Implementation of an intelligent sensor for measurement and prediction of solar radiation and atmospheric temperature

Gomes

Ferreira

Ruano

2011

2011 IEEE 7th International Symposium on Intelligent Signal Processing

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Daily solar irradiation estimation over a mountainous area using artificial neural networks

Cited by 110 publications

References 24 publications

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

Implementation of an intelligent sensor for measurement and prediction of solar radiation and atmospheric temperature

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