Abstract
The tilt angle of photovoltaic (PV) panels is a crucial determinant of their performance and can be adjusted using different tracking methods. Periodically changing the tilt angle strikes a practical balance between efficiency and cost. This work introduces a bi-directional long short-term memory (Bi-LSTM)-based direct normal irradiance (DNI) prediction to estimate the time intervals for the tilt angle adjustments. DNI prediction involves 22-year (2000–2022) historical time series data and the Bi-LSTM deep learning model to predict DNI at different time frames for the location Madurai, India. Using the predicted DNI, tilt angle-based DNI is mapped using the tilt angle correlation through a nearest neighborhood interpolation method. DNI potential over a specific period is utilized to find the optimum time intervals for the tilt angle adjustments. The simulation study of this work is implemented with a 5 kW grid-connected solar PV system using pvsyst software. The effectiveness of the proposed methodology is evaluated based on the improvements in power output, levelized cost of energy (LCOE), and carbon emission reductions and compared with other existing methods. The results showed that using the proposed optimal tilt angle intervals led to a 10.31% increase in PV output power, the lowest LCOE at 3.61 c/kW h, and 8.363 tCO2/year carbon emissions.
Issue Section:
Research Papers
References
1.
Gwesha
, A. O.
, Li
, P.
, and Alfulayyih
, Y. M.
, 2021
, “Optimization of Fixed Photovoltaic Panel Tilt Angles for Maximal Energy Harvest Considering Year-Around Sky Coverage Conditions
,” ASME J. Sol. Energy Eng.
, 143
(2
), p. 021002
. 2.
Rabaia
, M. K. H.
, Abdelkareem
, M. A.
, Sayed
, E. T.
, Elsaid
, K.
, Chae
, K. J.
, Wilberforce
, T.
, and Olabi
, A. G.
, 2021
, “Environmental Impacts of Solar Energy Systems: A Review
,” Sci. Total Environ.
, 754
, p. 141989
. 3.
Badhotiya
, G. K.
, Sharma
, V.
, Singh
, D. B.
, and Dobriyal
, R.
, 2021
, “Identification of Key Determinants of Solar Power System Adoption in India
,” Mater. Today: Proc.
, 46
(20
), pp. 10449
–10453
. 4.
Talaat
, M.
, Said
, T.
, Essa
, M. A.
, and Hatata
, A. Y.
, 2022
, “Integrated MFFNN-MVO Approach for PV Solar Power Forecasting Considering Thermal Effects and Environmental Conditions
,” Int. J. Electr. Power Energy Syst.
, 135
, p. 107570
. 5.
Sharma
, A.
, Kallioğlu
, M. A.
, Awasthi
, A.
, Chauhan
, R.
, Fekete
, G.
, and Singh
, T.
, 2021
, “Correlation Formulation for Optimum Tilt Angle for Maximizing the Solar Radiation on Solar Collector in the Western Himalayan Region
,” Case Stud. Therm. Eng.
, 26
, p. 101185
. 6.
Xu
, L.
, Long
, E.
, Wei
, J.
, Cheng
, Z.
, and Zheng
, H.
, 2021
, “A New Approach to Determine the Optimum Tilt Angle and Orientation of Solar Collectors in Mountainous Areas With High Altitude
,” Energy
, 237
, p. 121507
. 7.
Hafez
, A. Z.
, Soliman
, A.
, El-Metwally
, K. A.
, and Ismail
, I. M.
, 2017
, “‘Tilt and Azimuth Angles in Solar Energy Applications–A Review’
,” Renew. Sustain. Energy Rev.
, 77
, pp. 147
–168
. 8.
Ayaz
, R.
, Durusu
, A.
, and Akca
, H.
, 2017
, “Determination of Optimum Tilt Angle for Different Photovoltaic Technologies Considering Ambient Conditions: A Case Study for Burdur, Turkey
,” ASME J. Sol. Energy Eng.
, 139
(4
), p. 041001
. 9.
Bayrakçı
, H. C.
, Demircan
, C.
, and Keçebaş
, A.
, 2018
, “The Development of Empirical Models for Estimating Global Solar Irradiance on Horizontal Surface: A Case Study
,” Renew. Sustain. Energy Rev.
, 81
(2
), pp. 2771
–2782
. 10.
Lai
, J. P.
, Chang
, Y. M.
, Chen
, C. H.
, and Pai
, P. F.
, 2020
, “A Survey of Machine Learning Models in Renewable Energy Predictions
,” Appl. Sci.
, 10
(17
), p. 5975
. 11.
Kumar
, D.
, 2020
, “Satellite-Based Solar Energy Potential Analysis for Southern States of India
,” Energy Rep.
, 6
, pp. 1487
–1500
. 12.
Srivastava
, R. C.
, and Pandey
, H.
, 2013
, “Estimating Angstrom-Prescott Coefficients for India and Developing a Correlation Between Sunshine Hours and Global Solar Irradiance for India
,” Int. Scholarly Res. Not.
, 2013
(1
), p. 403742
. 13.
Khahro
, S. F.
, Tabbassum
, K.
, Talpur
, S.
, Alvi
, M. B.
, Liao
, X.
, and Dong
, L.
, 2015
, “Evaluation of Solar Energy Resources by Establishing Empirical Models for Diffuse Solar Radiation on Tilted Surface and Analysis for Optimum Tilt Angle for a Prospective Location in Southern Region of Sindh, Pakistan
,” Int. J. Electr. Power Energy Syst.
, 64
, pp. 1073
–1080
. 14.
Perez
, R.
, Lorenz
, E.
, Pelland
, S.
, Beauharnois
, M.
, Van Knowe
, G.
, Hemker Jr
, K.
, and Pomares
, L. M.
, 2013
, “Comparison of Numerical Weather Prediction Solar Irradiance Forecasts in the US, Canada and Europe
,” Sol. Energy
, 94
, pp. 305
–326
. 15.
Durrani
, S. P.
, Balluff
, S.
, Wurzer
, L.
, and Krauter
, S.
, 2018
, “Photovoltaic Yield Prediction Using an Irradiance Forecast Model Based on Multiple Neural Networks
,” J. Mod. Power Syst. Clean Energy
, 6
(2
), pp. 255
–267
. 16.
Ramu
, P.
, and Gangatharan
, S.
, 2023
, “An Ensemble Machine Learning-Based Solar Power Prediction of Meteorological Variability Conditions to Improve Accuracy in Forecasting
,” J. Chin. Inst. Eng.
, 46
(7
), pp. 737
–753
. 17.
Qing
, X.
, and Niu
, Y.
, 2018
, “Hourly Day-Ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM
,” Energy
, 148
, pp. 461
–468
. 18.
Cannizzaro
, D.
, Aliberti
, A.
, Bottaccioli
, L.
, Macii
, E.
, Acquaviva
, A.
, and Patti
, E.
, 2021
, “Solar Radiation Forecasting Based on Convolutional Neural Network and Ensemble Learning
,” Expert Syst. Appl.
, 181
, p. 115167
. 19.
Agbulut
, U.
, Gürel
, A. E.
, and Biçen
, Y.
, 2021
, “Prediction of Daily Global Solar Radiation Using Different Machine Learning Algorithms: Evaluation and Comparison
,” Renew. Sustain. Energy Rev.
, 135
, p. 110114
. 20.
Feng
, Y.
, Hao
, W.
, Li
, H.
, Cui
, N.
, Gong
, D.
, and Gao
, L.
, 2020
, “Machine Learning Models to Quantify and Map Daily Global Solar Irradiance and Photovoltaic Power
,” Renew. Sustain. Energy Rev.
, 118
, p. 109393
. 21.
Mousavi
, S. M.
, Mostafavi
, E. S.
, and Jiao
, P.
, 2017
, “Next Generation Prediction Model for Daily Solar Irradiance on Horizontal Surface Using a Hybrid Neural Network and Simulated Annealing Method
,” Energy Convers. Manage.
, 153
, pp. 671
–682
. 22.
Muniyandi
, V.
, Manimaran
, S.
, Ramu
, P. R.
, and Gangatharan
, S.
, 2023
, “A Comprehensive Analysis of Recent Advances in Deep Learning Based Solar Irradiance Forecasting
,” 7th International Conference on Trends in Electronics and Informatics (ICOEI)
, Tirunelveli, India
, Apr. 11–13
, pp. 1250
–1257
.23.
Luo
, X.
, Zhang
, D.
, and Zhu
, X.
, 2021
, “Deep Learning-Based Forecasting of Photovoltaic Power Generation by Incorporating Domain Knowledge
,” Energy
, 225
, p. 120240
. 24.
Tuncer
, E.
, and Bolat
, E. D.
, 2022
, “Classification of Epileptic Seizures From Electroencephalogram (EEG) Data Using Bidirectional Short-Term Memory (Bi-LSTM) Network Architecture
,” Biomed. Signal Process. Control
, 73
, p. 103462
. 25.
Ho-Huu
, V.
, Vo-Duy
, T.
, Luu-Van
, T.
, Le-Anh
, L.
, and Nguyen-Thoi
, T.
, 2016
, “Optimal Design of Truss Structures With Frequency Constraints Using Improved Differential Evolution Algorithm Based on an Adaptive Mutation Scheme
,” Autom. Constr.
, 68
, pp. 81
–94
. 26.
Ramu
, P.
, Gangatharan
, S.
, Rangasamy
, S.
, and Mihet-Popa
, L.
, 2023
, “Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique
,” Sustainability
, 15
(16
), p. 12209
. 27.
Cornejo-Bueno
, L.
, Casanova-Mateo
, C.
, Sanz-Justo
, J.
, and Salcedo-Sanz
, S.
, 2019
, “Machine Learning Regressors for Solar Irradiance Estimation From Satellite Data
,” Sol. Energy
, 183
, pp. 768
–775
. 28.
Ahmad
, M. J.
, and Tiwari
, G. N.
, 2009
, “Optimum Tilt Angle for Solar Collectors Used in India
,” Int. J. Ambient Energy
, 30
(2
), pp. 73
–78
. 29.
Yilmaz
, S.
, Ozcalik
, H. R.
, Dogmus
, O.
, Dincer
, F.
, Akgol
, O.
, and Karaaslan
, M.
, 2015
, “Design of Two Axes Sun Tracking Controller With Analytically Solar Radiation Calculations
,” Renew. Sustain. Energy Rev.
, 43
, pp. 997
–1005
. 30.
Ibnelouad
, A.
, El Kari
, A.
, Ayad
, H.
, and Mjahed
, M.
, 2020
, “Improved Cooperative Artificial Neural Network-Particle Swarm Optimization Approach for Solar Photovoltaic Systems Using Maximum Power Point Tracking
,” Int. Trans. Electr. Energy Syst.
, 30
(8
), p. 12439
. 31.
Vijay
, M.
, and Saravanan
, M.
, 2022
, “Solar Irradiance Forecasting Using Bayesian Optimization Based Machine Learning Algorithm to Determine the Optimal Size of a Residential PV System
,” International Conference on Sustainable Computing and Data Communication Systems
, Erode, India
, Apr. 7–9
, pp. 744
–749
(INSPEC Accession Number: 21725341).32.
Perveen
, G.
, Rizwan
, M.
, and Goel
, N.
, 2019
, “Comparison of Intelligent Modelling Techniques for Forecasting Solar Energy and Its Application in Solar PV Based Energy System
,” IET Energy Syst. Integr.
, 1
(1
), pp. 34
–51
. 33.
Husain
, S.
, and Khan
, U. A.
, 2021
, “Development of Machine Learning Models Based on Air Temperature for Estimation of Global Solar Radiation in India
,” Environ. Prog. Sustain. Energy
, 41
(4
), p. 13782
. 34.
Liu
, W.
, Li
, J.
, Li
, S.
, Luo
, J.
, and Jiang
, X.
, 2021
, “Research on Optimum Tilt Angle of Photovoltaic Module Based on Regional Clustering of Influencing Factors of Power Generation
,” Int. J. Energy Res.
, 45
(7
), pp. 11002
–11017
. 35.
Kaya
, F.
, Şahin
, G.
, and Alma
, M. H.
, 2021
, “Investigation Effects of Environmental and Operating Factors on PV Panel Efficiency Using by Multivariate Linear Regression
,” Int. J. Energy Res.
, 45
(1
), pp. 554
–567
. 36.
García-Hinde
, O.
, Terrén-Serrano
, G.
, Hombrados-Herrera
, M. Á.
, Gómez-Verdejo
, V.
, Jiménez-Fernández
, S.
, Casanova-Mateo
, C.
, and Salcedo-Sanz
, S.
, 2018
, “Evaluation of Dimensionality Reduction Methods Applied to Numerical Weather Models for Solar Radiation Forecasting
,” Eng. Appl. Artif. Intell.
, 69
, pp. 157
–167
. 37.
Garud
, K. S.
, Jayaraj
, S.
, and Lee
, M. Y.
, 2021
, “A Review on Modeling of Solar Photovoltaic Systems Using Artificial Neural Networks, Fuzzy Logic, Genetic Algorithm and Hybrid Models
,” Int. J. Energy Res.
, 45
(1
), pp. 6
–35
. 38.
Hou
, X.
, Ju
, C.
, and Wang
, B.
, 2023
, “Prediction of Solar Irradiance Using Convolutional Neural Network and Attention Mechanism-Based Long Short-Term Memory Network Based on Similar Day Analysis and an Attention Mechanism
,” Heliyon
, 9
(11
), p. 21484
. 39.
Muniyandi
, V.
, Manimaran
, S.
, and Balasubramanian
, A. K.
, 2023
, “Improving the Power Output of a Partially Shaded Photovoltaic Array Through a Hybrid Magic Square Configuration With Differential Evolution-Based Adaptive P&O MPPT Method
,” ASME J. Sol. Energy Eng.
, 145
(5
), p. 051001
. 40.
Irwana
, Y. M.
, Ameliaa
, A. R.
, Irwantoa
, M.
, Ma
, F.
, Leowa
, W. Z.
, Gomesha
, N.
, and Safwati
, I.
, 2015
, “Stand-Alone Photovoltaic (SAPV) System Assessment Using PVSYST Software
,” Energy Procedia
, 79
, pp. 596
–603
. 41.
Karabiber
, A.
, and Güneş
, Y.
, 2023
, “Single-Motor and Dual-Axis Solar Tracking System for Micro Photovoltaic Power Plants
,” ASME J. Sol. Energy Eng
, 145
(5
), p. 051004
. 42.
Sun
, X.
, Khan
, M. R.
, Deline
, C.
, and Alam
, M. A.
, 2018
, “Optimization and Performance of Bifacial Solar Modules: A Global Perspective
,” Appl. Energy
, 212
, pp. 1601
–1610
. 43.
Gupta
, A.
, Agrawal
, S.
, and Pal
, Y.
, 2020
, “Performance Evaluation of Hybrid Photovoltaic Thermal Thermoelectric Collector Using Grasshopper Optimization Algorithm With Simulated Annealing
,” ASME J. Sol. Energy Eng.
, 142
(6
), p. 061004
. 44.
Zabihi
, A.
, Parhamfar
, M.
, Duvvuri
, S. S.
, and Abtahi
, M.
, 2024
, “Increase Power Output and Radiation in Photovoltaic Systems by Installing Mirrors
,” Meas. Sens.
, 31
, p. 100946
. 45.
Maghrabie
, H. M.
, Mohamed
, A. S. A.
, and Salem Ahmed
, M.
, 2020
, “Experimental Investigation of a Combined Photovoltaic Thermal System Via Air Cooling for Summer Weather of Egypt
,” ASME J. Thermal Sci. Eng. Appl.
, 12
(4
), p. 041022
. 46.
Kazem
, H. A.
, Chaichan
, M. T.
, Al-Waeli
, A. H. A.
, and Sopian
, K.
, 2020
, “A Review of Dust Accumulation and Cleaning Methods for Solar Photovoltaic Systems
,” J. Cleaner Prod.
, 276
, p. 123187
. 47.
Maleki
, A.
, Haghighi
, A.
, El Haj Assad
, M.
, Mahariq
, I.
, and Alhuyi Nazari
, M.
, 2020
, “A Review on the Approaches Employed for Cooling PV Cells'
,” Sol. Energy
, 209
, pp. 170
–185
. 48.
Kazem
, H. A.
, Al-Waeli
, A. H. A.
, Chaichan
, M. T.
, Sopian
, K.
, Ahmed
, A. A.
, and Wan Nor Roslam
, W. I.
, 2023
, “Enhancement of Photovoltaic Module Performance Using Passive Cooling (Fins): A Comprehensive Review
,” Case Stud. Therm. Eng.
, 49
, p. 103316
. 49.
Abdulmunem
, A. R.
, Samin
, P. M.
, Rahman
, H. A.
, Hussien
, H. A.
, and Mazali
, I. I.
, 2020
, “Enhancing PV Cell's Electrical Efficiency Using Phase Change Material With Copper Foam Matrix and Multi-walled Carbon Nanotubes as Passive Cooling Method
,” Renew. Energy
, 160
, pp. 663
–675
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