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MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

Yıl 2023, Cilt: 24 Sayı: 2, 141 - 154, 21.06.2023

Kübra Kaysal Fatih Onur Hocaoğlu

https://doi.org/10.18038/estubtda.1184918

Öz

IIn recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.

Anahtar Kelimeler

Artificial neural networks Wavelet transform Mother wavelets Solar radiation estimation.

Kaynakça

  • [1] Acikgoz H. A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Applied Energy 2022; 305: 117912.
  • [2] Mohsenzadeh Karimi S, Mirzaei M, Dehghani A, Galavi H, Huang YF. Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation. Stochastic Environmental Research and Risk Assessment 2022; 1-15.
  • [3] Gabrali D. Modeling of wind and solar energy potential with artificial neural network and analysis with wavelet transformation. İstanbul Aydın University, Science Institute, Master's Thesis, 2019.
  • [4] Sun Y, Li H, Andlib Z, Genie MG. How do renewable energy and urbanization cause carbon emissions? Evidence from advanced panel estimation techniques. Renewable Energy 2022; 185: 996-1005.
  • [5] Akarslan E, Hocaoglu FO, Edizkan R. Novel short term solar irradiance forecasting models. Renewable Energy 2018; 123: 58-66.
  • [6] Sharifi SS, Rezaverdinejad V, Nourani V, Behmanesh J. Multi-time-step ahead daily global solar radiation5 forecasting: performance evaluation of wavelet-based artificial neural network model. Meteorology and Atmospheric Physics 2022; 134(3): 1-14.
  • [7] Qiu R, Li L, Wu L, Agathokleous E, Liu C, Zhang B, Sun S. Modeling daily global solar radiation using only temperature data: Past, development, and future. Renewable and Sustainable Energy Reviews 2022; 163: 112511.
  • [8] Hocaoğlu FO. Stochastic approach for daily solar radiation modeling. Solar Energy 2011; 85(2): 278-287.
  • [9] Guermoui M, Abdelaziz R, Gairaa K, Djemoui L, Benkaciali S. New temperature-based predicting model for global solar radiation using support vector regression. International Journal of Ambient Energy 2022; 43(1): 1397-1407.
  • [10] Mohammadi K, Shamshirband S, Tong CW, Arif M, Petković D, Ch S. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management 2015; 92: 162-171.
  • [11] Falayi EO, Adepitan JO, Oni OO, Faseyi MT. Wavelet power spectrum analysis applied for solar radiation investigations over Nigeria. Songklanakarin Journal of Science & Technology 2021; 43:4.
  • [12] Ferkous K, Chellali F, Kouzou A, Bekkar B. Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria. Journal homepage: http://iieta. org/journals/i2m 2021; 20(2): 113-119.
  • [13] Belmahdi B, Louzazni M, El Bouardi A. One month-ahead forecasting of mean daily global solar radiation using time series models. Optik 2020; 219: 165207.
  • [14] Rabehi A, Guermoui M, Lalmi D. Hybrid models for global solar radiation prediction: a case study. International Journal of Ambient Energy 2020; 41: 31-40.
  • [15] Faisal AF, Rahman A, Habib MTM, Siddique AH, Hasan M, Khan MM. Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh. Results in Engineering 2022; 13: 100365.
  • [16] Belmahdi B, Louzazni M, El Bouardi A. Comparative optimization of global solar radiation forecasting using machine learning and time series models. Environmental Science and Pollution Research 2022; 29: 14871-14888.
  • [17] Malik P, Gehlot A, Singh R, Gupta LR, Thakur AK. A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction with Real-Time Data. Archives of Computational Methods in Engineering 2022; 1:19.
  • [18] Monjoly S, André M, Calif R, Soubdhan T. Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy 2017; 119: 288-298.
  • [19] Wang F, Mi Z, Su S, Zhao H. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 2012; 5(5):1355-1370.
  • [20] Wang H, Cai R, Zhou B, Aziz S, Qin B, Voropai N, Barakhtenko E. Solar irradiance forecasting based on direct explainable neural network. Energy Conversion and Management 2020; 226: 113487.
  • [21] Husein M, Chung IY. Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: A deep learning approach. Energies 2019; 12(10): 1856.
  • [22] Mutavhatsindi T, Sigauke C, Mbuvha R. Forecasting hourly global horizontal solar irradiance in South Africa using machine learning models. IEEE Access 2020; 8: 198872-198885.
  • [23] Singla P, Duhan M, Saroha S. A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM). Arabian Journal for Science and Engineering 2022; 1-27.
  • [24] Meng F, Zou Q, Zhang Z, Wang B, Ma H, Abdullah HM, Mohamed MA. An intelligent hybrid wavelet-adversarial deep model for accurate prediction of solar power generation. Energy Reports 2021; 7: 2155-2164.
  • [25] Guermoui M, Gairaa K, Boland J, Arrif T. A novel hybrid model for solar radiation forecasting using support vector machine and bee colony optimization algorithm: review and case study. Journal of Solar Energy Engineering 2021; 143(2).
  • [26] Huang X, Li Q, Tai Y, Chen Z, Zhang J, Shi J, Liu W. Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy 2021; 171: 1041-1060.
  • [27] Farzanehdehkordi M, Ghaffaripour S, Tirdad K, Cruz AD, Sadeghian A. A wavelet feature-based neural network approach to estimate electrical arc characteristics. Electric Power Systems Research 2022; 208: 107893.
  • [28] Öner İV, Yeşilyurt MK, Yılmaz E. Wavelet Analysis Technique and Application Areas. Ordu University, Journal of Science and Technology 2017; 7: 42-56.
  • [29] Peng L, Wang L, Xia D, Gao. Effective energy consumption forecasting using empirical wavelet transform and long short-term memory. Energy 2022; 238: 121756.
  • [30] Osadchiy A, Kamenev A, Saharov V, Chernyi S. Signal processing algorithm based on discrete wavelet transform. Designs 2021; 5(3): 41.
  • [31] Nourani V, Komasi M, Mano A. A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water resources management 2019; 23(14): 2877-2894.
  • [32] Alsafadi M, Filik ÜB. Hourly global solar radiation estimation based on machine learning methods in Eskısehır. Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering 2020; 21(2): 294-313.
  • [33] Kaysal A, Köroglu S, Oguz Y, Kaysal K. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems Approaches to Forecast the Electricity Data for Load Demand, an Analysis of Dinar District Case. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 2018; (pp. 1-6). IEEE.
  • [34] Partal T. Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data. KSCE Journal of Civil Engineering 2016; 20(5): 2050-2058.

MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

Yıl 2023, Cilt: 24 Sayı: 2, 141 - 154, 21.06.2023

Kübra Kaysal Fatih Onur Hocaoğlu

https://doi.org/10.18038/estubtda.1184918

Öz

In recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.

Anahtar Kelimeler

Artificial neural networks Wavelet transform Mother wavelets Solar radiation estimation

Kaynakça

  • [1] Acikgoz H. A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Applied Energy 2022; 305: 117912.
  • [2] Mohsenzadeh Karimi S, Mirzaei M, Dehghani A, Galavi H, Huang YF. Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation. Stochastic Environmental Research and Risk Assessment 2022; 1-15.
  • [3] Gabrali D. Modeling of wind and solar energy potential with artificial neural network and analysis with wavelet transformation. İstanbul Aydın University, Science Institute, Master's Thesis, 2019.
  • [4] Sun Y, Li H, Andlib Z, Genie MG. How do renewable energy and urbanization cause carbon emissions? Evidence from advanced panel estimation techniques. Renewable Energy 2022; 185: 996-1005.
  • [5] Akarslan E, Hocaoglu FO, Edizkan R. Novel short term solar irradiance forecasting models. Renewable Energy 2018; 123: 58-66.
  • [6] Sharifi SS, Rezaverdinejad V, Nourani V, Behmanesh J. Multi-time-step ahead daily global solar radiation5 forecasting: performance evaluation of wavelet-based artificial neural network model. Meteorology and Atmospheric Physics 2022; 134(3): 1-14.
  • [7] Qiu R, Li L, Wu L, Agathokleous E, Liu C, Zhang B, Sun S. Modeling daily global solar radiation using only temperature data: Past, development, and future. Renewable and Sustainable Energy Reviews 2022; 163: 112511.
  • [8] Hocaoğlu FO. Stochastic approach for daily solar radiation modeling. Solar Energy 2011; 85(2): 278-287.
  • [9] Guermoui M, Abdelaziz R, Gairaa K, Djemoui L, Benkaciali S. New temperature-based predicting model for global solar radiation using support vector regression. International Journal of Ambient Energy 2022; 43(1): 1397-1407.
  • [10] Mohammadi K, Shamshirband S, Tong CW, Arif M, Petković D, Ch S. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management 2015; 92: 162-171.
  • [11] Falayi EO, Adepitan JO, Oni OO, Faseyi MT. Wavelet power spectrum analysis applied for solar radiation investigations over Nigeria. Songklanakarin Journal of Science & Technology 2021; 43:4.
  • [12] Ferkous K, Chellali F, Kouzou A, Bekkar B. Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria. Journal homepage: http://iieta. org/journals/i2m 2021; 20(2): 113-119.
  • [13] Belmahdi B, Louzazni M, El Bouardi A. One month-ahead forecasting of mean daily global solar radiation using time series models. Optik 2020; 219: 165207.
  • [14] Rabehi A, Guermoui M, Lalmi D. Hybrid models for global solar radiation prediction: a case study. International Journal of Ambient Energy 2020; 41: 31-40.
  • [15] Faisal AF, Rahman A, Habib MTM, Siddique AH, Hasan M, Khan MM. Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh. Results in Engineering 2022; 13: 100365.
  • [16] Belmahdi B, Louzazni M, El Bouardi A. Comparative optimization of global solar radiation forecasting using machine learning and time series models. Environmental Science and Pollution Research 2022; 29: 14871-14888.
  • [17] Malik P, Gehlot A, Singh R, Gupta LR, Thakur AK. A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction with Real-Time Data. Archives of Computational Methods in Engineering 2022; 1:19.
  • [18] Monjoly S, André M, Calif R, Soubdhan T. Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy 2017; 119: 288-298.
  • [19] Wang F, Mi Z, Su S, Zhao H. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 2012; 5(5):1355-1370.
  • [20] Wang H, Cai R, Zhou B, Aziz S, Qin B, Voropai N, Barakhtenko E. Solar irradiance forecasting based on direct explainable neural network. Energy Conversion and Management 2020; 226: 113487.
  • [21] Husein M, Chung IY. Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: A deep learning approach. Energies 2019; 12(10): 1856.
  • [22] Mutavhatsindi T, Sigauke C, Mbuvha R. Forecasting hourly global horizontal solar irradiance in South Africa using machine learning models. IEEE Access 2020; 8: 198872-198885.
  • [23] Singla P, Duhan M, Saroha S. A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM). Arabian Journal for Science and Engineering 2022; 1-27.
  • [24] Meng F, Zou Q, Zhang Z, Wang B, Ma H, Abdullah HM, Mohamed MA. An intelligent hybrid wavelet-adversarial deep model for accurate prediction of solar power generation. Energy Reports 2021; 7: 2155-2164.
  • [25] Guermoui M, Gairaa K, Boland J, Arrif T. A novel hybrid model for solar radiation forecasting using support vector machine and bee colony optimization algorithm: review and case study. Journal of Solar Energy Engineering 2021; 143(2).
  • [26] Huang X, Li Q, Tai Y, Chen Z, Zhang J, Shi J, Liu W. Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy 2021; 171: 1041-1060.
  • [27] Farzanehdehkordi M, Ghaffaripour S, Tirdad K, Cruz AD, Sadeghian A. A wavelet feature-based neural network approach to estimate electrical arc characteristics. Electric Power Systems Research 2022; 208: 107893.
  • [28] Öner İV, Yeşilyurt MK, Yılmaz E. Wavelet Analysis Technique and Application Areas. Ordu University, Journal of Science and Technology 2017; 7: 42-56.
  • [29] Peng L, Wang L, Xia D, Gao. Effective energy consumption forecasting using empirical wavelet transform and long short-term memory. Energy 2022; 238: 121756.
  • [30] Osadchiy A, Kamenev A, Saharov V, Chernyi S. Signal processing algorithm based on discrete wavelet transform. Designs 2021; 5(3): 41.
  • [31] Nourani V, Komasi M, Mano A. A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water resources management 2019; 23(14): 2877-2894.
  • [32] Alsafadi M, Filik ÜB. Hourly global solar radiation estimation based on machine learning methods in Eskısehır. Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering 2020; 21(2): 294-313.
  • [33] Kaysal A, Köroglu S, Oguz Y, Kaysal K. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems Approaches to Forecast the Electricity Data for Load Demand, an Analysis of Dinar District Case. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 2018; (pp. 1-6). IEEE.
  • [34] Partal T. Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data. KSCE Journal of Civil Engineering 2016; 20(5): 2050-2058.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Kübra Kaysal 0000-0003-3983-2608

Fatih Onur Hocaoğlu 0000-0002-3640-7676

Yayımlanma Tarihi 21 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 24 Sayı: 2

Kaynak Göster

AMA Kaysal K, Hocaoğlu FO. MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. Haziran 2023;24(2):141-154. doi:10.18038/estubtda.1184918