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Year 2024, Volume: 10 Issue: 5, 1164 - 1183, 10.09.2024

Abstract

References

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  • [2] BP. Statistical review of world energy 2021. Available at: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf. Accessed Aug 7, 2024.
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  • [4] Kaiser P, Unde RB, Kern C, Jess A. Production of liquid hydrocarbons with CO2 as carbon source based on reverse water-gas shift and Fischer-Tropsch synthesis. Chem Ing Tech 2013;85:489–499. [CrossRef]
  • [5] Ali S, Taweekun J, Techato K, Waewsak J, Gyawali S. GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand. Renew Energy 2019;132:1360–1372. [CrossRef]
  • [6] Noorollahi Y, Yousefi H, Mohammadi M. Multi-criteria decision support system for wind farm site selection using GIS. Sustain Energy Technol Assess 2016;13:38–50. [CrossRef]
  • [7] Aydin NY, Kentel E, Sebnem Duzgun H. GIS-based site selection methodology for hybrid renewable energy systems: A case study from western Turkey. Energy Conver Manage 2013;70:90–106. [CrossRef]
  • [8] Heras-Saizarbitoria I, Cilleruelo E, Zamanillo I. Public acceptance of renewables and the media: An analysis of the Spanish PV solar experience. Renew Sustain Energy Rev 2011;15:4685–4696. [CrossRef]
  • [9] Tabassum A, Premalatha M, Abbasi T, Abbasi SA. Wind energy: Increasing deployment, rising environmental concerns. Renew Sustain Energy Rev 2014;31:270–288. [CrossRef]
  • [10] Ministry of Renewable Energy, India. Solar – Current Status. Available at: www.mnre.gov.in/solar/current-status. Accessed Dec 23, 2021.
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  • [14] Yan J, Hu L, Zhen Z, Qiu G, Li Y, Yao L, et al. Frequency-domain decomposition and deep learning based solar PV power ultra-short-term forecasting model. IEEE Trans Ind Appl 2021;57:328232-92. [CrossRef]
  • [15] Ahmed R, Sreeram V, Mishra Y, Arif MD. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renew Sustain Energy Rev 2020;124:109792. [CrossRef]
  • [16] Mellit A, Kalogirou SA. Artificial intelligence techniques for photovoltaic applications: A review. Prog Energy Combust Sci 2008;34:574–632. [CrossRef]
  • [17] Bhowmik M, Naik BK, Muthukumar P, Anandalakshmi R. Performance assessment and optimization of liquid desiccant dehumidifier system using intelligent models and integration with solar dryer. J Build Engineer 2023;64:105577. [CrossRef]
  • [18] Bhowmik M, Muthukumar P, Anandalakshmi R. Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions. Renew Energy 2019;143:1566–1580. [CrossRef]
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  • [20] Kim YS, Joo HY, Kim JW, Jeong SY, Moon JH. Use of a big data analysis in regression of solar power generation on meteorological variables for a Korean solar power plant. Appl Sci 2021;11:1776. [CrossRef]
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  • [22] Smithson SC, Yang G, Gross WJ, Meyer BH. Neural networks designing neural networks: Multi-objective hyper-parameter optimization. IEEE/ACM International Conference on Computer - Aided Design; 2016. pp. 18. [CrossRef]
  • [23] Sundaram S, Babu JSC. Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India. Energy Conver Manage 2015;100:429–439. [CrossRef]
  • [24] Yadav SK, Bajpai U. Performance evaluation of a rooftop solar photovoltaic power plant in Northern India. Energy Sustain Dev 2018;43:130–138. [CrossRef]
  • [25] Kymakis E, Kalykakis S, Papazoglou TM. Performance analysis of a grid connected photovoltaic park on the island of Crete. Energy Conver Manage 2009;50:433–438. [CrossRef]
  • [26] Ayompe LM, Duffy A, McCormack SJ, Conlon M. Measured performance of a 1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Conver Manage 2011;52:816–825. [CrossRef]
  • [27] Kumar M, Kumar A. Performance assessment and degradation analysis of solar photovoltaic technologies: A review. Renew Sustain Energy Rev 2017;78:554–587. [CrossRef]
  • [28] Saitoh H, Hamada Y, Kubota H, Nakamura M, Ochifuji K, Yokoyama S, et al. Field experiments and analyses on a hybrid solar collector. Appl Therm Engineer 2003;23:2089–2105. [CrossRef]
  • [29] Sundaram S. Performance assessment with the prediction of final yield and performance ratio employing artificial neural network for a realistic 1 MWp PV plant in India. Int J Ambient Energy 2022;43:1739–1750. [CrossRef]
  • [30] Kumar BS, Sudhakar K. Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Rep 2015;1:184–192. [CrossRef]
  • [31] Padmavathi K, Daniel SA. Performance analysis of a 3 MWp grid connected solar photovoltaic power plant in India. Energy Sustain Dev 2013;17:615–625. [CrossRef]

Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation

Year 2024, Volume: 10 Issue: 5, 1164 - 1183, 10.09.2024

Abstract

Energy and Exergy based performetric analysis integrated with deep learning assisted energy modelling for grid connected solar PV system, tested to non-trained location is proposed. The first objective is to perform an energy and exergy based performetric analysis for a realistic 380 kWp grid connected roof-top PV system whose performance parameter is used for testing the proposed energy prediction models. The second objective is to formulate a simple and an improved energy estimation method applicable for 34 locations in South India, without change in model-coefficients. So, a long-term annual performance analysis of a 380 kWp PV based distributed generator situated at 12.97°N and 77.59°E is performed which estimates the characteristic performance indicators like energy efficiency, exergy efficiency, performance ratio and capacity factor amounting to 8.49%, 1.03%, 37%, and 8.03% respectively. The performance ratio of the plant is less as evident from the least exergy efficiency. The annual average losses in the system like thermal capture loss, array capture loss, system loss and miscellaneous loss amount to 0.46 (h/d), 2.51(h/d), 0.71 (h/d) and 2.97(h/d) respectively. The annual average energy generation of 380 kWp is 732.84 kWh/year. Furthermore, for realizing the second objective, a total of four models are proposed namely linear, exponential, non-linear and deep learning based neural network model resulting in R of 0.933, 0.9071, 0.9386, and 0.9603 respectively is formulated. The proposed models are tested for non-trained locations where the R value justifying the closeness between the actual and the predicted value is as high as 0.8. The proposed models are then compared upon their performances and benchmarked against the reported models.

References

  • [1] IEA. India energy policy review 2020. Available at: https://niti.gov.in/sites/default/files/2020-01/IEA-India 2020-In-depth-EnergyPolicy_0.pdf. Accessed Aug 7, 2024.
  • [2] BP. Statistical review of world energy 2021. Available at: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf. Accessed Aug 7, 2024.
  • [3] Uyan M. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renew Sustain Energy Rev 2013;28:11–17. [CrossRef]
  • [4] Kaiser P, Unde RB, Kern C, Jess A. Production of liquid hydrocarbons with CO2 as carbon source based on reverse water-gas shift and Fischer-Tropsch synthesis. Chem Ing Tech 2013;85:489–499. [CrossRef]
  • [5] Ali S, Taweekun J, Techato K, Waewsak J, Gyawali S. GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand. Renew Energy 2019;132:1360–1372. [CrossRef]
  • [6] Noorollahi Y, Yousefi H, Mohammadi M. Multi-criteria decision support system for wind farm site selection using GIS. Sustain Energy Technol Assess 2016;13:38–50. [CrossRef]
  • [7] Aydin NY, Kentel E, Sebnem Duzgun H. GIS-based site selection methodology for hybrid renewable energy systems: A case study from western Turkey. Energy Conver Manage 2013;70:90–106. [CrossRef]
  • [8] Heras-Saizarbitoria I, Cilleruelo E, Zamanillo I. Public acceptance of renewables and the media: An analysis of the Spanish PV solar experience. Renew Sustain Energy Rev 2011;15:4685–4696. [CrossRef]
  • [9] Tabassum A, Premalatha M, Abbasi T, Abbasi SA. Wind energy: Increasing deployment, rising environmental concerns. Renew Sustain Energy Rev 2014;31:270–288. [CrossRef]
  • [10] Ministry of Renewable Energy, India. Solar – Current Status. Available at: www.mnre.gov.in/solar/current-status. Accessed Dec 23, 2021.
  • [11] IEA; International Renewable Energy Agency; United Nations Statistics Division; The World Bank; World Health Organization. Tracking SDG 7: The energy progress report - 2019. Available at: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/May/2019-Tracking-SDG7-Report.pdf?rev=30fdda95ad074b6eb27c8aa4347605e8. Accessed Aug 7, 2024.
  • [12] Huang Q, Wei S. Improved quantile convolutional neural network with two-stage training for daily-ahead probabilistic forecasting of photovoltaic power. Energy Conver Manage 2020;220:113085. [CrossRef]
  • [13] Meng X, Gao F, Xu T, Zhou K, Wei L, Wu Q. Inverter-data-driven second-level power forecasting for photovoltaic power plant. IEEE Trans Ind Electron 2020;68:7034–7044. [CrossRef]
  • [14] Yan J, Hu L, Zhen Z, Qiu G, Li Y, Yao L, et al. Frequency-domain decomposition and deep learning based solar PV power ultra-short-term forecasting model. IEEE Trans Ind Appl 2021;57:328232-92. [CrossRef]
  • [15] Ahmed R, Sreeram V, Mishra Y, Arif MD. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renew Sustain Energy Rev 2020;124:109792. [CrossRef]
  • [16] Mellit A, Kalogirou SA. Artificial intelligence techniques for photovoltaic applications: A review. Prog Energy Combust Sci 2008;34:574–632. [CrossRef]
  • [17] Bhowmik M, Naik BK, Muthukumar P, Anandalakshmi R. Performance assessment and optimization of liquid desiccant dehumidifier system using intelligent models and integration with solar dryer. J Build Engineer 2023;64:105577. [CrossRef]
  • [18] Bhowmik M, Muthukumar P, Anandalakshmi R. Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions. Renew Energy 2019;143:1566–1580. [CrossRef]
  • [19] Baharin KA, Rahman HA, Hassan MY, Gan CK. Short-term forecasting of solar photovoltaic output power for tropical climate using ground-based measurement data. J Renew Sustain Energy 2016;8:053701. [CrossRef]
  • [20] Kim YS, Joo HY, Kim JW, Jeong SY, Moon JH. Use of a big data analysis in regression of solar power generation on meteorological variables for a Korean solar power plant. Appl Sci 2021;11:1776. [CrossRef]
  • [21] Sheng H, Xiao J, Cheng Y, Ni Q, Wang S. Short-term solar power forecasting based on weighted gaussian process regression. IEEE Trans Ind Electron 2018;65:300–308. [CrossRef]
  • [22] Smithson SC, Yang G, Gross WJ, Meyer BH. Neural networks designing neural networks: Multi-objective hyper-parameter optimization. IEEE/ACM International Conference on Computer - Aided Design; 2016. pp. 18. [CrossRef]
  • [23] Sundaram S, Babu JSC. Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India. Energy Conver Manage 2015;100:429–439. [CrossRef]
  • [24] Yadav SK, Bajpai U. Performance evaluation of a rooftop solar photovoltaic power plant in Northern India. Energy Sustain Dev 2018;43:130–138. [CrossRef]
  • [25] Kymakis E, Kalykakis S, Papazoglou TM. Performance analysis of a grid connected photovoltaic park on the island of Crete. Energy Conver Manage 2009;50:433–438. [CrossRef]
  • [26] Ayompe LM, Duffy A, McCormack SJ, Conlon M. Measured performance of a 1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Conver Manage 2011;52:816–825. [CrossRef]
  • [27] Kumar M, Kumar A. Performance assessment and degradation analysis of solar photovoltaic technologies: A review. Renew Sustain Energy Rev 2017;78:554–587. [CrossRef]
  • [28] Saitoh H, Hamada Y, Kubota H, Nakamura M, Ochifuji K, Yokoyama S, et al. Field experiments and analyses on a hybrid solar collector. Appl Therm Engineer 2003;23:2089–2105. [CrossRef]
  • [29] Sundaram S. Performance assessment with the prediction of final yield and performance ratio employing artificial neural network for a realistic 1 MWp PV plant in India. Int J Ambient Energy 2022;43:1739–1750. [CrossRef]
  • [30] Kumar BS, Sudhakar K. Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Rep 2015;1:184–192. [CrossRef]
  • [31] Padmavathi K, Daniel SA. Performance analysis of a 3 MWp grid connected solar photovoltaic power plant in India. Energy Sustain Dev 2013;17:615–625. [CrossRef]
There are 31 citations in total.

Details

Primary Language English
Subjects Thermodynamics and Statistical Physics
Journal Section Articles
Authors

Abhijeet Rathore This is me 0000-0002-7037-0264

- Almas This is me 0009-0001-1193-6715

Sivasankari Sundaram This is me 0000-0003-2470-3387

Publication Date September 10, 2024
Submission Date December 26, 2022
Published in Issue Year 2024 Volume: 10 Issue: 5

Cite

APA Rathore, A., Almas, .-., & Sundaram, S. (2024). Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation. Journal of Thermal Engineering, 10(5), 1164-1183.
AMA Rathore A, Almas, Sundaram S. Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation. Journal of Thermal Engineering. September 2024;10(5):1164-1183.
Chicago Rathore, Abhijeet, - Almas, and Sivasankari Sundaram. “Energy, Exergy and Performance Analysis of a 380 KWP Roof-Top PV Plant Assisted With Data-Driven Models for Energy Generation”. Journal of Thermal Engineering 10, no. 5 (September 2024): 1164-83.
EndNote Rathore A, Almas -, Sundaram S (September 1, 2024) Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation. Journal of Thermal Engineering 10 5 1164–1183.
IEEE A. Rathore, .-. Almas, and S. Sundaram, “Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation”, Journal of Thermal Engineering, vol. 10, no. 5, pp. 1164–1183, 2024.
ISNAD Rathore, Abhijeet et al. “Energy, Exergy and Performance Analysis of a 380 KWP Roof-Top PV Plant Assisted With Data-Driven Models for Energy Generation”. Journal of Thermal Engineering 10/5 (September 2024), 1164-1183.
JAMA Rathore A, Almas -, Sundaram S. Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation. Journal of Thermal Engineering. 2024;10:1164–1183.
MLA Rathore, Abhijeet et al. “Energy, Exergy and Performance Analysis of a 380 KWP Roof-Top PV Plant Assisted With Data-Driven Models for Energy Generation”. Journal of Thermal Engineering, vol. 10, no. 5, 2024, pp. 1164-83.
Vancouver Rathore A, Almas -, Sundaram S. Energy, exergy and performance analysis of a 380 kWP roof-top PV plant assisted with data-driven models for energy generation. Journal of Thermal Engineering. 2024;10(5):1164-83.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK http://eds.yildiz.edu.tr/journal-of-thermal-engineering