Energy-exergy modeling of solar radiation with most influencing input parameters

Taki, Morteza; Rohani, Abbas; Yıldızhan, Hasan; Farhadi, Rouhollah

doi:10.1080/15567036.2018.1550126

Cited by 6 publications

(3 citation statements)

References 54 publications

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“…Another numerical-intelligent hybrid forecasting model connects the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS), and also with hybrid firefly and particle swarm optimization algorithm (HFPSO) (Abdullah et al, 2019;Karan, 2019;Lund et al, 2019;Taki et al, 2019), where the wavelet transform reduces noise in both the meteorological and solar power data. ANFIS is the predictor, whereas the HFPSO is the combination of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is engaged in optimizing the input parameters of the ANFIS to enhance the accuracy (Oldewurtel et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Savarimuthu,

Victor,

Davaraj

et al. 2024

JST

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The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Savarimuthu,

Victor,

Davaraj

et al. 2024

JST

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“…Arslanoglu [26] n, N Regression Mohammed and Mengüç [27] n, N, T 0 , v Regression Taki et al [59] T ave , T min , T max , RH, P, TST, WS Gaussian process regression Jamil and Bellos [28] n, N, K T Regression Khorasanizadeh and Sepehrnia [29] n, N Regression…”

Section: Author Variables Analysismentioning

confidence: 99%

“…Taki et al [59] used a soft computing Gaussian process regression to model total solar radiation and solar radiation exergy. For the case of exergy, the author considered the Petela model to link this model with average T ave , minimum T min and maximum T max temperature, average relative humidity RH, pressure P, total sunbathing time TST and average wind speed WS in the Hakkari province (Turkey).…”

Section: Author Variables Analysismentioning

confidence: 99%

Modelling the Exergy of Solar Radiation: A Review

et al. 2022

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Exergy is a thermodynamic property that represents the quantification of the maximum useful work that can be extracted from a system interacting with the environment. Regarding solar radiation, radiative exergy has been a matter of study over the last 60 years where the main models applied describe the radiation as undiluted and diluted. The exergy of solar radiation is useful in the preliminary assessment of the performance of solar technologies, since the efficiency of the system depends directly on this value. The present paper describes a review of the main models reported in the literature considering these two approaches, analysing the main differences between the models and the main assumptions applied. A comparative analysis is carried out for the models of diluted and undiluted radiation, where the behaviour of every expression is discussed in detail. For the undiluted expressions, the behaviour of every model within a temperature range is analysed. For black-body radiation at a source temperature of 6000K, the model proposed by Jeter determines an exergy factor of 0.96, while Spanner, Petela, Press and Badescu calculate a value of 0.93. Parrott’s model obtains a value of 0.99, which is above the value for Carnot efficiency. The diluted exergy expressions were evaluated according to wavelength and temperature range, where the trend in each comparison was that the exergy calculated from Karlsson, Candau and Petela was always the lowest. This result is attributed to the fact that these expressions consider the spectral entropy of the medium the radiation passes through. Finally, some new approaches are analysed which consider empirical correlations based on meteorological variables to model the exergy of solar radiation.

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