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Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm

Yıl 2024, Cilt: 6 Sayı: 1, 24 - 34, 30.04.2024

Yasemin Ayaz Atalan

https://doi.org/10.46387/bjesr.1377273

Öz

The efficiency of solar energy systems requires a complicated forecasting process due to the variability of sunlight and environmental conditions. Among environmental factors, cloud coverage (% range), temperature (0C), wind speed (Mph), and humidity (%) variables were taken into account in this study. Neural networks (NN), which are machine learning (ML) algorithms with a flexible structure that can define complex relationships and process large amounts of data for solar energy prediction, were used in this study. The NN algorithm showed a high performance, with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values calculated as 0.019, 0.139, 0.053, and 0.977, respectively. This study emphasized that solar energy predictions made with the NN algorithm, considering environmental factors, are an essential tool that helps use solar energy systems more efficiently and sustainably.

Anahtar Kelimeler

Machine Learning Neural Network Algorithm Solar Energy Environmental Factors

Kaynakça

  • Y.A. Atalan and A. Atalan, “Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy,” Sustainability, vol. 15, no. 18, p. 13782, Sep. 2023.
  • N. Fatima, Y. Li, M. Ahmad, G. Jabeen, and X. Li, “Factors influencing renewable energy generation development: a way to environmental sustainability,” Environ. Sci. Pollut. Res., vol. 28, no. 37, pp. 51714–51732, 2021.
  • V. Mhasawade, Y. Zhao, and R. Chunara, “Machine learning and algorithmic fairness in public and population health,” Nat. Mach. Intell., vol. 3, no. 8, pp. 659–666, Aug. 2021.
  • V. Ramanathan and Y. Feng, “Air pollution, greenhouse gases and climate change: Global and regional perspectives,” Atmos. Environ., vol. 43, no. 1, pp. 37–50, 2009.
  • L. Qi and Y. Zhang, “Effects of solar photovoltaic technology on the environment in China,” Environ. Sci. Pollut. Res., vol. 24, pp. 22133–22142, 2017.
  • A. Sharif, S.A. Raza, I. Ozturk, and S. Afshan, “The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations,” Renew. energy, vol. 133, pp. 685–691, 2019.
  • F. Dincer, “The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy,” Renew. Sustain. energy Rev., vol. 15, no. 1, pp. 713–720, 2011.
  • N. Armaroli and V. Balzani, “The future of energy supply: challenges and opportunities,” Angew. Chemie Int. Ed., vol. 46, no. 1‐2, pp. 52–66, 2007.
  • S. Kanwal, M.T. Mehran, M. Hassan, M. Anwar, S. R. Naqvi, and A.H. Khoja, “An integrated future approach for the energy security of Pakistan: Replacement of fossil fuels with syngas for better environment and socio-economic development,” Renew. Sustain. Energy Rev., vol. 156, p. 111978, 2022.
  • U. Pelay, L. Luo, Y. Fan, D. Stitou, and M. Rood, “Thermal energy storage systems for concentrated solar power plants,” Renew. Sustain. Energy Rev., vol. 79, pp. 82–100, 2017.
  • F. Trieb and H. Elnokraschy, “Concentrating solar power for seawater desalination,” IWCT, vol. 12, pp. 2–13, 2007.
  • A.C. Şerban and M.D. Lytras, “Artificial intelligence for smart renewable energy sector in europe—smart energy infrastructures for next generation smart cities,” IEEE access, vol. 8, pp. 77364–77377, 2020.
  • A. Mohammad and F. Mahjabeen, “Revolutionizing Solar Energy: The Impact of Artificial Intelligence on Photovoltaic Systems,” Int. J. Multidiscip. Sci. Arts, vol. 2, no. 1, 2023.
  • A. Gopi, P. Sharma, K. Sudhakar, W. K. Ngui, I. Kirpichnikova, and E. Cuce, “Weather impact on solar farm performance: A comparative analysis of machine learning techniques,” Sustainability, vol. 15, no. 1, p. 439, 2022.
  • M.M.V. Cantarero, “Of renewable energy, energy democracy, and sustainable development: A roadmap to accelerate the energy transition in developing countries,” Energy Res. Soc. Sci., vol. 70, p. 101716, 2020.
  • K. Mahmud, S. Azam, A. Karim, S. Zobaed, B. Shanmugam, and D. Mathur, “Machine learning based PV power generation forecasting in alice springs,” IEEE Access, vol. 9, pp. 46117–46128, 2021.
  • A. Atalan, H. Şahin, and Y.A. Atalan, “Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources,” Healthcare, vol. 10, no. 10, p. 1920, Sep. 2022.
  • F. Rodríguez, A. Fleetwood, A. Galarza, and L. Fontán, “Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control,” Renew. energy, vol. 126, pp. 855–864, 2018.
  • R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization,” Renew. Sustain. Energy Rev., vol. 124, p. 109792, 2020.
  • O. Erdinc and M. Uzunoglu, “Optimum design of hybrid renewable energy systems: Overview of different approaches,” Renew. Sustain. Energy Rev., vol. 16, no. 3, pp. 1412–1425, 2012.
  • Z. He, W. Guo, and P. Zhang, “Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods,” Renew. Sustain. Energy Rev., vol. 156, p. 111977, 2022.
  • I.M. Galván, J.M. Valls, A. Cervantes, and R. Aler, “Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks,” Inf. Sci. (Ny)., vol. 418–419, pp. 363–382, 2017.
  • A. Kuzmiakova, G. Colas, and A. McKeehan, “Predicting solar energy using machine learning: CS 229 project,” 2023. https://github.com/adelekuzmiakova/CS229machine-learning-solar-energy-predictions/tree/master
  • H. İnaç, Y.E. Ayözen, A. Atalan, and C.Ç. Dönmez, “Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms,” Appl. Sci., vol. 12, no. 23, p. 12266, Nov. 2022.
  • J. Li, M.S. Herdem, J. Nathwani, and J.Z. Wen, “Methods and applications for artificial intelligence, big data, internet-of-things, and blockchain in smart energy management,” Energy AI, p. 100208, 2022.
  • S. Osama, H. Shaban, and A.A. Ali, “Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review,” Expert Syst. Appl., vol. 213, p. 118946, 2023.
  • A. Abraham et al., “Machine learning for neuroimaging with scikit-learn,” Front. Neuroinform., vol. 8, p. 14, 2014.
  • K. Hsu, S. Levine, and C. Finn, “Unsupervised learning via meta-learning,” arXiv Prepr. arXiv1810.02334, 2018.
  • R. Diao, Z. Wang, D. Shi, Q. Chang, J. Duan, and X. Zhang, “Autonomous voltage control for grid operation using deep reinforcement learning,” in 2019 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2019, pp. 1–5.
  • J. Yang et al., “Neuromorphic engineering: from biological to spike‐based hardware nervous systems,” Adv. Mater., vol. 32, no. 52, p. 2003610, 2020.
  • S. Boccaletti et al., “The structure and dynamics of multilayer networks,” Phys. Rep., vol. 544, no. 1, pp. 1–122, 2014.
  • M. Khandelwal and T.N. Singh, “Prediction of blast-induced ground vibration using artificial neural network,” Int. J. Rock Mech. Min. Sci., vol. 46, no. 7, pp. 1214–1222, 2009.
  • D. Graupe, Principles of Artificial Neural Networks, vol. 7. in Advanced Series in Circuits and Systems, vol. 7. World Scıentıfıc, 2013.
  • C. Coman, L.G. Țîru, L. Meseșan-Schmitz, C. Stanciu, and M.C. Bularca, “Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective,” Sustainability, vol. 12, no. 24, p. 10367, 2020.
  • V. Nasteski, “An overview of the supervised machine learning methods,” Horizons. b, vol. 4, pp. 51–62, 2017.
  • S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward,” PLoS One, vol. 13, no. 3, p. e0194889, 2018.
  • Y.-C. Wang and J.M. Usher, “Application of reinforcement learning for agent-based production scheduling,” Eng. Appl. Artif. Intell., vol. 18, no. 1, pp. 73–82, 2005.
  • E.L. Jacobsen and J. Teizer, “Deep learning in construction: Review of applications and potential avenues,” J. Comput. Civ. Eng., vol. 36, no. 2, p. 3121001, 2022.
  • C. Zhang and Y. Lu, “Study on artificial intelligence: The state of the art and future prospects,” J. Ind. Inf. Integr., vol. 23, p. 100224, 2021.
  • L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. big Data, vol. 8, pp. 1–74, 2021.
  • D.P. Kiley, S. Haley, B. Saylor, and B.L. Saylor, “The Value of Evidence-Based Computer Simulation of Oral Health Outcomes for Management Analysis of the Alaska Dental Health Aide Program,” Institute of Social and Economic Research, University of Alaska Anchorage, 2008. [Online]. Available: http://hdl.handle.net/11122/4459
  • M.S. Lewis-Beck and A. Skalaban, “The R-squared: Some straight talk,” Polit. Anal., vol. 2, pp. 153–171, 1990. A. Atalan, “Statistical optimization of forecast data from Adaptive Boosting and Support Vector Machine Algorithms,” in International Conference on Engineering, Natural and Social Sciences, pp. 571–579. 2023,
  • C.J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Clim. Res., vol. 30, no. 1, pp. 79–82, 2005.
  • D. Chicco, M.J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, p. e623, 2021.
  • A. Atalan, “Effect of healthcare expendıture on the correlatıon between the number of nurses and doctors employed,” ınt. J. Heal. Manag. Tour., vol. 6, no. 2, pp. 515–525, 2021.
  • D.B. Figueiredo Filho, J. A. S. Júnior, and E. C. Rocha, “What is R2 all about?,” Leviathan (São Paulo), no. 3, pp. 60–68, 2011.
  • N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, “Predicting solar generation from weather forecasts using machine learning,” in 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, Oct. 2011, pp. 528–533.
  • A. Gensler, J. Henze, B. Sick, and N. Raabe, “Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Oct. 2016, pp. 002858–002865.
  • Z. Li, S. Rahman, R. Vega, and B. Dong, “A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting,” Energies, vol. 9, no. 1, p. 55, 2016.

Sinir ağı algoritması ile çevresel faktörler kapsamında Güneş Enerjisinin tahmin edilmesi

Yıl 2024, Cilt: 6 Sayı: 1, 24 - 34, 30.04.2024

Yasemin Ayaz Atalan

https://doi.org/10.46387/bjesr.1377273

Öz

Güneş enerjisi sistemlerinin verimliliği, güneş ışığının ve çevre koşullarının değişkenliği nedeniyle karmaşık bir tahmin süreci gerektirmektedir. Bu çalışmada çevresel faktörlerden bulut kapsamı (% aralık), hava sıcaklığı (0C), rüzgar hızı (Mph) ve bağıl nem (%) değişkenleri dikkate alınmıştır. Bu çalışmada güneş enerjisi tahmini için karmaşık ilişkileri tanımlayabilen ve büyük miktarda veriyi işleyebilen esnek yapıya sahip makine öğrenmesi (ML) algoritmalarından sinir ağı (NN) kullanılmıştır. NN algoritması, ortalama kare hatası (MSE), kök ortalama kare hatası (RMSE), ortalama mutlak hatası (MAE), ve R-kare (R2) değerleri sırasıyla 0,019, 0,139 0,053 ve 0,977 olarak hesaplanarak yüksek bir performans göstermiştir. Bu çalışmada, çevresel faktörler dikkate alınarak NN algoritması ile yapılan güneş enerjisi tahminlerinin, güneş enerjisi sistemlerinin daha verimli ve sürdürülebilir kullanılmasına yardımcı olan önemli bir araç olduğu vurgulanmıştır.

Anahtar Kelimeler

Makine Öğrenimi Sinir Ağı Algoritması Güneş Enerjisi Çevresel Faktörler

Kaynakça

  • Y.A. Atalan and A. Atalan, “Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy,” Sustainability, vol. 15, no. 18, p. 13782, Sep. 2023.
  • N. Fatima, Y. Li, M. Ahmad, G. Jabeen, and X. Li, “Factors influencing renewable energy generation development: a way to environmental sustainability,” Environ. Sci. Pollut. Res., vol. 28, no. 37, pp. 51714–51732, 2021.
  • V. Mhasawade, Y. Zhao, and R. Chunara, “Machine learning and algorithmic fairness in public and population health,” Nat. Mach. Intell., vol. 3, no. 8, pp. 659–666, Aug. 2021.
  • V. Ramanathan and Y. Feng, “Air pollution, greenhouse gases and climate change: Global and regional perspectives,” Atmos. Environ., vol. 43, no. 1, pp. 37–50, 2009.
  • L. Qi and Y. Zhang, “Effects of solar photovoltaic technology on the environment in China,” Environ. Sci. Pollut. Res., vol. 24, pp. 22133–22142, 2017.
  • A. Sharif, S.A. Raza, I. Ozturk, and S. Afshan, “The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations,” Renew. energy, vol. 133, pp. 685–691, 2019.
  • F. Dincer, “The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy,” Renew. Sustain. energy Rev., vol. 15, no. 1, pp. 713–720, 2011.
  • N. Armaroli and V. Balzani, “The future of energy supply: challenges and opportunities,” Angew. Chemie Int. Ed., vol. 46, no. 1‐2, pp. 52–66, 2007.
  • S. Kanwal, M.T. Mehran, M. Hassan, M. Anwar, S. R. Naqvi, and A.H. Khoja, “An integrated future approach for the energy security of Pakistan: Replacement of fossil fuels with syngas for better environment and socio-economic development,” Renew. Sustain. Energy Rev., vol. 156, p. 111978, 2022.
  • U. Pelay, L. Luo, Y. Fan, D. Stitou, and M. Rood, “Thermal energy storage systems for concentrated solar power plants,” Renew. Sustain. Energy Rev., vol. 79, pp. 82–100, 2017.
  • F. Trieb and H. Elnokraschy, “Concentrating solar power for seawater desalination,” IWCT, vol. 12, pp. 2–13, 2007.
  • A.C. Şerban and M.D. Lytras, “Artificial intelligence for smart renewable energy sector in europe—smart energy infrastructures for next generation smart cities,” IEEE access, vol. 8, pp. 77364–77377, 2020.
  • A. Mohammad and F. Mahjabeen, “Revolutionizing Solar Energy: The Impact of Artificial Intelligence on Photovoltaic Systems,” Int. J. Multidiscip. Sci. Arts, vol. 2, no. 1, 2023.
  • A. Gopi, P. Sharma, K. Sudhakar, W. K. Ngui, I. Kirpichnikova, and E. Cuce, “Weather impact on solar farm performance: A comparative analysis of machine learning techniques,” Sustainability, vol. 15, no. 1, p. 439, 2022.
  • M.M.V. Cantarero, “Of renewable energy, energy democracy, and sustainable development: A roadmap to accelerate the energy transition in developing countries,” Energy Res. Soc. Sci., vol. 70, p. 101716, 2020.
  • K. Mahmud, S. Azam, A. Karim, S. Zobaed, B. Shanmugam, and D. Mathur, “Machine learning based PV power generation forecasting in alice springs,” IEEE Access, vol. 9, pp. 46117–46128, 2021.
  • A. Atalan, H. Şahin, and Y.A. Atalan, “Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources,” Healthcare, vol. 10, no. 10, p. 1920, Sep. 2022.
  • F. Rodríguez, A. Fleetwood, A. Galarza, and L. Fontán, “Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control,” Renew. energy, vol. 126, pp. 855–864, 2018.
  • R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization,” Renew. Sustain. Energy Rev., vol. 124, p. 109792, 2020.
  • O. Erdinc and M. Uzunoglu, “Optimum design of hybrid renewable energy systems: Overview of different approaches,” Renew. Sustain. Energy Rev., vol. 16, no. 3, pp. 1412–1425, 2012.
  • Z. He, W. Guo, and P. Zhang, “Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods,” Renew. Sustain. Energy Rev., vol. 156, p. 111977, 2022.
  • I.M. Galván, J.M. Valls, A. Cervantes, and R. Aler, “Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks,” Inf. Sci. (Ny)., vol. 418–419, pp. 363–382, 2017.
  • A. Kuzmiakova, G. Colas, and A. McKeehan, “Predicting solar energy using machine learning: CS 229 project,” 2023. https://github.com/adelekuzmiakova/CS229machine-learning-solar-energy-predictions/tree/master
  • H. İnaç, Y.E. Ayözen, A. Atalan, and C.Ç. Dönmez, “Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms,” Appl. Sci., vol. 12, no. 23, p. 12266, Nov. 2022.
  • J. Li, M.S. Herdem, J. Nathwani, and J.Z. Wen, “Methods and applications for artificial intelligence, big data, internet-of-things, and blockchain in smart energy management,” Energy AI, p. 100208, 2022.
  • S. Osama, H. Shaban, and A.A. Ali, “Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review,” Expert Syst. Appl., vol. 213, p. 118946, 2023.
  • A. Abraham et al., “Machine learning for neuroimaging with scikit-learn,” Front. Neuroinform., vol. 8, p. 14, 2014.
  • K. Hsu, S. Levine, and C. Finn, “Unsupervised learning via meta-learning,” arXiv Prepr. arXiv1810.02334, 2018.
  • R. Diao, Z. Wang, D. Shi, Q. Chang, J. Duan, and X. Zhang, “Autonomous voltage control for grid operation using deep reinforcement learning,” in 2019 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2019, pp. 1–5.
  • J. Yang et al., “Neuromorphic engineering: from biological to spike‐based hardware nervous systems,” Adv. Mater., vol. 32, no. 52, p. 2003610, 2020.
  • S. Boccaletti et al., “The structure and dynamics of multilayer networks,” Phys. Rep., vol. 544, no. 1, pp. 1–122, 2014.
  • M. Khandelwal and T.N. Singh, “Prediction of blast-induced ground vibration using artificial neural network,” Int. J. Rock Mech. Min. Sci., vol. 46, no. 7, pp. 1214–1222, 2009.
  • D. Graupe, Principles of Artificial Neural Networks, vol. 7. in Advanced Series in Circuits and Systems, vol. 7. World Scıentıfıc, 2013.
  • C. Coman, L.G. Țîru, L. Meseșan-Schmitz, C. Stanciu, and M.C. Bularca, “Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective,” Sustainability, vol. 12, no. 24, p. 10367, 2020.
  • V. Nasteski, “An overview of the supervised machine learning methods,” Horizons. b, vol. 4, pp. 51–62, 2017.
  • S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward,” PLoS One, vol. 13, no. 3, p. e0194889, 2018.
  • Y.-C. Wang and J.M. Usher, “Application of reinforcement learning for agent-based production scheduling,” Eng. Appl. Artif. Intell., vol. 18, no. 1, pp. 73–82, 2005.
  • E.L. Jacobsen and J. Teizer, “Deep learning in construction: Review of applications and potential avenues,” J. Comput. Civ. Eng., vol. 36, no. 2, p. 3121001, 2022.
  • C. Zhang and Y. Lu, “Study on artificial intelligence: The state of the art and future prospects,” J. Ind. Inf. Integr., vol. 23, p. 100224, 2021.
  • L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. big Data, vol. 8, pp. 1–74, 2021.
  • D.P. Kiley, S. Haley, B. Saylor, and B.L. Saylor, “The Value of Evidence-Based Computer Simulation of Oral Health Outcomes for Management Analysis of the Alaska Dental Health Aide Program,” Institute of Social and Economic Research, University of Alaska Anchorage, 2008. [Online]. Available: http://hdl.handle.net/11122/4459
  • M.S. Lewis-Beck and A. Skalaban, “The R-squared: Some straight talk,” Polit. Anal., vol. 2, pp. 153–171, 1990. A. Atalan, “Statistical optimization of forecast data from Adaptive Boosting and Support Vector Machine Algorithms,” in International Conference on Engineering, Natural and Social Sciences, pp. 571–579. 2023,
  • C.J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Clim. Res., vol. 30, no. 1, pp. 79–82, 2005.
  • D. Chicco, M.J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, p. e623, 2021.
  • A. Atalan, “Effect of healthcare expendıture on the correlatıon between the number of nurses and doctors employed,” ınt. J. Heal. Manag. Tour., vol. 6, no. 2, pp. 515–525, 2021.
  • D.B. Figueiredo Filho, J. A. S. Júnior, and E. C. Rocha, “What is R2 all about?,” Leviathan (São Paulo), no. 3, pp. 60–68, 2011.
  • N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, “Predicting solar generation from weather forecasts using machine learning,” in 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, Oct. 2011, pp. 528–533.
  • A. Gensler, J. Henze, B. Sick, and N. Raabe, “Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Oct. 2016, pp. 002858–002865.
  • Z. Li, S. Rahman, R. Vega, and B. Dong, “A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting,” Energies, vol. 9, no. 1, p. 55, 2016.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Yasemin Ayaz Atalan 0000-0001-7767-0342

Erken Görünüm Tarihi 27 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 17 Ekim 2023
Kabul Tarihi 12 Aralık 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 1

Kaynak Göster

APA Ayaz Atalan, Y. (2024). Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 6(1), 24-34. https://doi.org/10.46387/bjesr.1377273
AMA Ayaz Atalan Y. Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm. Müh.Bil.ve Araş.Dergisi. Nisan 2024;6(1):24-34. doi:10.46387/bjesr.1377273
Chicago Ayaz Atalan, Yasemin. “Estimating Solar Energy Within the Scope of Environmental Factors by the Neural Network Algorithm”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 6, sy. 1 (Nisan 2024): 24-34. https://doi.org/10.46387/bjesr.1377273.
EndNote Ayaz Atalan Y (01 Nisan 2024) Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm. Mühendislik Bilimleri ve Araştırmaları Dergisi 6 1 24–34.
IEEE Y. Ayaz Atalan, “Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm”, Müh.Bil.ve Araş.Dergisi, c. 6, sy. 1, ss. 24–34, 2024, doi: 10.46387/bjesr.1377273.
ISNAD Ayaz Atalan, Yasemin. “Estimating Solar Energy Within the Scope of Environmental Factors by the Neural Network Algorithm”. Mühendislik Bilimleri ve Araştırmaları Dergisi 6/1 (Nisan 2024), 24-34. https://doi.org/10.46387/bjesr.1377273.
JAMA Ayaz Atalan Y. Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm. Müh.Bil.ve Araş.Dergisi. 2024;6:24–34.
MLA Ayaz Atalan, Yasemin. “Estimating Solar Energy Within the Scope of Environmental Factors by the Neural Network Algorithm”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, c. 6, sy. 1, 2024, ss. 24-34, doi:10.46387/bjesr.1377273.
Vancouver Ayaz Atalan Y. Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm. Müh.Bil.ve Araş.Dergisi. 2024;6(1):24-3.
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