Research Article Modeling of Daily Solar Energy System Prediction using Soft Computing Methods for Oman

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1 Research Journal of Appled Scences, Engneerng and Technology 13(3): , 2016 DOI: /rjaset ISSN: ; e-issn: Maxwell Scentfc Publcaton Corp. Submtted: January 26, 2016 Accepted: May 23, 2016 Publshed: August 05, 2016 Research Artcle Modelng of Daly Solar Energy System Predcton usng Soft Computng Methods for Oman Jabar H. Yousf and Hussen A. Kazem Sohar Unversty, P.O. Box 44, PCI 311, Sohar, Sultanate of Oman Abstract: The am of ths study s to desgn and mplement soft computng technques called Support Vector Machne (SVM) and Multlayer Perceptron (MLP) for great management of energy generaton based on expermental work. Solar energy could be utlzed through thermal systems or Photovoltacs (PV) and t s renewable energy source, envronmental frendly and proven globally for a long tme. The SVM and MLP models are consst of two nputs layers and one layer output. The nputs of SVM network are solar radaton and tme, whle the output s the PV current. The nputs of MLP network are solar radaton and ambent temperature, whle the output s the PV current. The practcal mplementaton of the proposed SVM model s acheved a fnal MSE of ( ) n tranng phase and ( ) n cross valdaton phase. Besdes, MLP s acheved a fnal MSE of ( ) n the tranng phase and t s acheved ( ) n cross valdaton phase. The fnal MSE of cross valdaton wth standard devaton s ( ). The experments acheved n the predctng model a value of determnaton factor (R² = ) for SVM and (R² = ) for MLP whch ndcates the predctng model s very close to the regresson lne and a well data fttng to the statstcal model. Besdes, the proposed model acheved less MSE n comparson wth other related work. Keywords: Machne learnng, Oman, solar energy predcton, support vector machne INTRODUCTION The growng n populaton and ndustry ncreased the energy needs. The energy ncrement leads to ncrease energy prces, whch reflected on many economes and energy shortages n some countres. From the other hand ncrease fossl fuel prces, clmate treates and polces enhanced the need to look for alternatve energy sources. Renewable energy could be the soluton for many reasons examples are: t s free, envronmental frendly, avalablty, unlmted, etc. Solar energy could be the most mportant renewable energy type, whch utlzed usng thermal systems or Photovoltacs (PV). Operatng cost of PV s very low, snce no fuel to be consumed, but ther peak power producton can be only recognzed on a clear sky, wth the PV facng the sun. Standby power generators or storage systems are needed n some cases because of the sun lght ntermttence, whch ncrease the energy system cost. The exceptons are n case of grd connected system or PV used for peak load at peak solar nsolaton. The growth n Oman populaton and ndustry reflected on energy needs and t could be clear on Peak power demand (MW) Year Fg. 1: Oman peak power demand for and projecton tll 2018 electrcty needs and t wll keep ncreasng n the near future (Fg. 1). The maxmum power demand expected to ncrease from 5,691 MW n 2015 to reach 6,000 n the mddle of In 2018 the forecasted power demand s 6.8 GW, whch mean more energy sources and power plants need to be nstalled (Kazem, 2011; Authorty for Electrcty Regulaton, Oman, 2008). In Oman the power plants manly used natural gas and a lttle desel, especally n rural areas, as fuel. To meet Correspondng Author: Hussen A. Kazem, Sohar Unversty, P.O. Box 44, PCI 311, Sohar, Sultanate of Oman, Tel.: ; Fax: Ths work s lcensed under a Creatve Commons Attrbuton 4.0 Internatonal Lcense (URL: 237

2 the electrcty needs nvest n renewable energy s most n Oman. The feasblty of renewable energy n Oman has been nvestgated n many studes. It s found that solar energy s promsng sources n the country. The demand s hgh n north part of Oman due to the ndustral areas and populaton, whch s approxmately 55% of populaton, where t s found that the potental of solar energy n Oman partcularly n ths regon. The day n Oman s long wth 10 to 12 h, 342 shnng days and free land to nstall PV systems (Altunkaynak and Özger, 2004; Fakham et al., 2011). Machne learnng s a subdvson of artfcal ntellgence that s developed as a result of studes n pattern classfcaton and recognton for fndng mathematcal models for varous real lfe problems. Machne learnng nvestgates the constructon of algorthms that can learn from the prevous data and help n fndng a forecast on the data n the current and future tme. The machne learnng appled teratve and nteractve statstcal methods n the constructon of computatonal models to obtan the desred results (Chen et al., 2011). The factors lke effcency of learnng algorthms, the complexty of the problem, methods of representaton of data are the most mportant factors affect the accuracy of the results and future forecastng for data. Ths study ams to dscuss and mplement machne learnng methods for great management of energy generaton of PV system. Several machne learnng methods are used for desgnng and mplementng dfferent phases of a renewable energy power systems based on the problem requrements and ts characterstcs (Schölkopf and Smola, 2011; Yousf, 2013). Hence, the adaptaton of optmal locaton and structures of renewable power plants s one of the mportant mplementaton of learnng machne methods. Recently, the Support Vector Machnes (SVMs) have been wdely mplemented nto several problems of renewable energy power systems (Banda et al., 2014; Sharma et al., 2011; Hossan et al., 2012). In ths study SVM model wll be proposed for solar system n Sohar, the second largest cty n Oman based on expermental data. Ths research has been based on data for solar rradatons and nstalled PV system n Oman. These data were provded by the Solar Cells and Photovoltac Research Lab n Sohar Unversty. In a solar cell or PV, the energy of sunlght n the form of photons s mbbed by the semconductor and then voltage s produced. The un-drectonal flow of these electrons across the cell creates a drect current, DC. PV technology s actually old. Solar energy technology and especally photovoltac has been expermented for more than 70 years and t shows good success. The AER Authorty of Electrcty Regulaton publshed ther frst study related to renewable energy n 2008, whch has explored there newable energy Res. J. Appl. Sc. Eng. Technol., 13(3): , (a) Fg. 2: (a): Grd connected PV system; (b): Meteorologcal staton sources n Oman. They clamed that solar than wnd could be the prortes of Oman renewable energy sources (Authorty for Electrcty Regulaton, Oman, 2008). A country wde ste selecton study was conducted wth an assessment of a number of potental stes based on a set of selecton crtera suted to potental solar stes has been conducted by PAEW Publc Authorty for Electrcty and Water n In 2011 the Research Councl of Oman granted SU Sohar Unversty project to nvestgate, desgn and assess PV systems n term of techncal and economc crtera for Oman. The feasblty study of PV systems n Oman standalone and grd connected are conducted n references (Kazem et al., 2011, 2014), respectvely. Kazem et al. (2013) proposed optmzaton of the PV system tlt angle, array sze and storage battery capacty usng MATLAB numercal method. Load demand and hourly meteorologcal data has been used. It s found that the PV system szng rato for PV array and battery are 1.33 and 1.6, respectvely. Also, they clamed that the cost of energy for standalone PV system n Oman s USD/kWh. Kazem et al. (2014) nvestgated and assessed the grd connected PV system n Oman. It s found that the Crst Factor and Yeld Factor, two techncal crtera, of the grd connected PV system are 21%, 1875 kwh/kwp/year respectvely. Meanwhle, the cost of the energy and the payback perod, economcal crtera, are USD/kWh and 11 years, respectvely. As a concluson they clamed that the energy generated by PV systems s cheaper than energy generated by fossl fuel n Oman. In the current study 24 PV module have been nstalled n Sohar Unversty n Oman. The ratng of PV module s 140W, 17.7 V maxmum voltages, 7.91 A maxmum current, 22.1 V open crcut voltage, 8.68 A short crcut current and 13.9% effcency. Three PV systems confguratons; standalone, grd connected and trackng systems have been desgned and evaluated (Fg. 2a). The meteorologcal data has been measured and recorded usng weather staton (Fg. 2b). The grd connected system data (voltage, current, power and energy) has been recorded and montored. Also, the measured envronmental parameters are lnked to the productvty of the PV system. (b)

3 Res. J. Appl. Sc. Eng. Technol., 13(3): , 2016 Fg. 3: Solar photovoltac grd connected system Ths research s a part of a research on grd connected PV system (Fg. 3) and consequently the producton of the PV system here s needed. Ths study proposed model predcts the output of grd connected PV system n Oman. The proposed model s based on mplementaton of artfcal ntellgent technques. The SVM s used to classfy and predct the future amount of producton of the PV system. The system expermental data has been measured n Sohar, Oman. LITERATURE REVIEW component analyss has been used to mprove the model. Sharma et al. (2011) proposed hybrd ntellgent predctor. The proposed system used regresson models namely, RBF, MLP, Lnear Regresson (LR), SVM, Smple Lnear Regresson (SLR), Pace Regresson (PR), Addtve Regresson (AR), Medan Square (LMS), IBk (an mplementaton of knn) and Locally Weghted Learnng (LWL). They clamed that LMS, MLP and SVM are the most accurate models n term of MAE and MAPE. Hossan et al. (2012), whch used SVM model to predct solar energy, found that SVM accuracy s less than Gaussan Process Regresson method. Chen et al. (2011) mplements a SVM model to estmate the daly solar radaton usng ar temperatures. The developed SVM model used a polynomal kernel functon whch performed better than other SVM models. He obtaned a hghest NSE of and the R-square of 0.969, whle the lowest RMSE s and RRMSE of The multple lnear regressons are adopted after checkng wth lnear models. All the varables are nvestgated for the functonal analyss of the varables. Some predcators lke day number wth sunshne rato are nvestgated usng smple multple regresson approach. In multple lnear models varous nputs are gven wth processng logc wth actvaton functon to get the desred output. Asl et al. (2011) mplements a predctng daly global solar radaton model based on meteorologcal varables, usng MLP neural networks. The author clamed that MAPE, R-square and MSE are 6.08, and %, respectvely. Hontora et al. (2005) proposed solar radaton maps for Span usng MLP. A MLP has been traned wth hourly solar radaton data of stes n Span. It s observed that usng exogenous varables mproves sgnfcantly the results for MLP. Table 1 shows a comparson of some MLP proposed models n lterature n term of error. It s clear seen that errors assocated wth predctons (monthly, daly, hourly and mnute) are between 4 and 10%. In sum, the use MLP represents a large majorty of research works. Photovoltac systems qute relable and has been well tested n space and terrestral applcatons. The doubt of PV systems output power s the man drawback of these systems. Therefore ths subject has stmulated the researchers to gve more focus on fndng the soluton for ths problem ether by proposng optmzaton methods or ncorporatng hybrd energy sources. Because of PV systems output power uncertanty, many attempts proposed to predct power productvty. In general these attempts are classfed nto; statstcal models based on tme seres of data, regresson based models, emprcal mathematcal models and fnally artfcal ntellgent neural network based models. There s number of studes n lterature related to the use of machne learnng technques to predct PV performance (Banda et al., 2014; Sharma et al., 2011; Hossan et al., 2012; Caudll and Butler, 1993), ncludng MultLayer Perceptron (MLP) network, Hetero Assocatve Neural Network, Probablstc Neural Networks (PNN), The Self-Organzng Map (SOM), General Regresson Neural Networks (GRNN), Recurrent Neural Networks, Hebban Neural Networks, Adaptve Neural Network Support Vector Machne based Radal Bass Functon networks (RBF), Generalzed Learnng Vector Quantzaton (GLVQ) and Hybrd Networks (Banda et al., 2014). Caudll and Butler (1993) proposed predcton model for solar power generaton based on expermental work. Dfferent machne learnng technques has been used. The authors ncluded SVM n the multple regresson MATERIALS AND METHODS technques. In SVM model they tred polynomal, lnear, RBF kernels. They clamed that SVM model Soft computng paradgms: Soft Computng (SC) s a accuracy ncreased up to 27%. Furthermore, prncpal new computng technque for utlzng real world 239

4 Res. J. Appl. Sc. Eng. Technol., 13(3): , 2016 Table 1: Comparson of MLP proposed models n lterature Authors Locaton Error Chaabene and Benammar (2008) Tunsa (Energy and Thermal Research Centre) nmbe= %, nrmse<10 % Jang (2008) Chna (8 ctes) Accuracy = 95 % Reddy and Ranjan (2003) Inda (2 statons) MAPE = 4 % Elmnr et al. (2007) Egypt (3 statons) Standard error = 4.2% Standard error = 9% Sözen et al. (2004) Turkey (17 statons) MAPE< 7% Fg. 4: MLP archtecture problems and provdes lower cost solutons. It s manly consstng of the followng technques: neural networks, fuzzy systems and evolutonary computaton. The three technques of soft computng are dffer from one another n ther functon and tme scales of operaton whch they embed a pror knowledge. The Neural networks mplements n a numerc framework, whch used to dentfy ther learnng and generalzaton condtons. Fuzzy systems are mplemented n a lngustc framework whch use to handle lngustc nformaton and then performs approxmate reasonng. Nevertheless, the evolutonary computaton technques are powerful methods for searchng and optmzng the results. Many researchers all over the world contrbuted essentally n soft computng to dscover solutons for varous problems n the modern scentfc socety applcatons (Beyer and Schwefel, 2002; Da et al., 2011; Prathar, 2007, 2013; Mellt et al., 2005). The sgnfcant drectons of soft computng applcatons are mplemented and performed nto knowledge representaton, learnng methods, path plannng, control, coordnaton and decson makng. Moreover, the SC can be sgnfcantly mplemented n the followng areas: The bometrcs systems, the bonformatcs systems, the bomedcal systems, the Robotcs applcatons, Vulnerablty analyss. Furthermore, SC s performed successfully n Character recognton, Data mnng, Natural Language Processng (NLP) (Yousf, 2013), Mult-objectve optmzatons, Wreless networks, Fnancal and tme seres predcton, Image processng, Toxcology, Machne control, Software engneerng, Informaton management, Pcture compresson, Nose removal and Socal network analyss (Prathar, 2007), etc. Neural networks: A neural network s consdered as a data processng technque that maps an nput of a stream of nformaton to an output stream of data. Artfcal Neural Network s a powerful data-modelng tool, whch can perform complex nput/output relatonshps ether lnear or non-lnear. The most popular ANN technques are Multlayer Perceptron (MLP) as depcted n Fg. 4. ANN has a number of features that can encourage scentst to mplement Neural Network desgn themes n dfferent applcatons. The most sgnfcant characterstcs of ANN are ablty of parallelsm, unformty; t can learn from tranng sets, t can be generalzed to adopt new data and adaptve ablty (Yousf, 2011). ANN s consstng of three processng layers, nput, hdden and output. They connected together usng weghted lnks. The archtecture of neural network s the assocaton of neurons nto layers and the connecton pattern wthn and between layers. It s llustrated as R-S1-S2-S3, whch means that the nput layer comprses of R nputs and connected to hdden layers. Ths network conssts of two hdden layers S1 and S2. Ths archtecture has only one output layer S3. The weghts of nput and hdden layers determne when each hdden layer s actvated. The hdden layers are n turn connected to output layers. The transfer functon and weghts of each neuron t should be defned. The tranng process s used to adjust the weghts of the ANN to match set of samples (tranng set). SVM&MLP confguraton: The SVM and MLP network are desgned and mplemented usng a Neuro soluton package. Each of them has only one hdden layer and one output layer. Besdes, two nputs layers are mplemented. The SVM archtecture has nput data sets (the tme stamp and correspondng UPV-Solar) and t has one output data set (Photovoltac temperature). The data undertaken are generated from 24-PV modules whch have been nstalled at Sohar Unversty n Oman. 240

5 Res. J. Appl. Sc. Eng. Technol., 13(3): , 2016 The nputs of MLP network layers consst of solar radaton and ambent temperature, whle the output s the value of current photovoltac. The experment nvolved of 1000 epochs. In order to regulate the range of each neuron between [-1 and 1], a TANH transfer functon s appled. The back propagaton learnng algorthm (BP) s used to adapt the errors through the layers of the network for adjustng of weghts n the hdden layer. BP learnng functon s computed as follows: E w = d p y p (1) where, E (w) : Error functon to be mnmzed w : Weght vector pt : Number of tranng patterns epoch : Number of output neurons d(p) : Desred output of neuron y(p) :A output of the neuron The computng of new weght vector w s repeated n tranng phase untl the error functon s become a small value and then stopped the recursve computng. Ths means that the network output s closer to the desred output. The present value of the weght s computed as follows: +1 = + + j (2) where, the local error s computed from at the output layer or can be computed as a weghted sum of errors at the nternal layers. The constant step sze s. The Momentum learnng s used to speed up and stablze convergence of network. The update of the weghts n momentum learnng s computed as follows: +1 = (3) desred output (d)), the correlaton coeffcent (r) s used. Normally, the value of r s n the range of [-1, 1]. If r = 1, ths ndcates a strong postve relatonshp (correlaton). When r = 0, ths gve ndcate that there s no correlaton between these varables. However, f r = -1, then a negatve correlaton between these varables s regstered. The correlaton s computed as n Eq. (5): r = ( x x) 2 ( d d) ( x x) N N ( d d ) N 2 (5) Besdes, the coeffcent of determnaton R 2 s used to assess the performance of the proposed predctng system and how t s well fttng to the actual results. The coeffcent of determnaton R 2 s defned n Eq. (6): =1 (6) where, : The observed value of the actual output f : The predcted value : The arthmetc mean value of the observed targets. The better a model s wll lkely predct future outcomes more precsely, whch s acheved by value of R 2 closer to 1. RESULTS AND DISCUSSION Fgure 5 demonstratons the tranng and cross valdaton MSE of the MLP network, whch s clearly ndcatng the graph lne of tranng data set n lne wth The best value of α n the momentum s between 0.1 and 0.9. In the current study, the experments gve evdence that the best value for α s 0.7. The Mean Square Error (MSE) s used to measure the varaton of the predcted values from the measured data, whch s computed as follows: MSE= 1 n n = 1 ( I p I ) (4) Where I s the measured value, Ip s the predcted value and n s the number of observatons. Moreover, n order to determne the relaton between two varables (a network output (x) and a 241 Fg. 5: Average MSE of MLP wth standard devaton boundares for 3 runs

6 Res. J. Appl. Sc. Eng. Technol., 13(3): , 2016 Table 2: Best results of MLP Networks All runs Tranng mnmum Tranng SD Cross valdaton mnmum Cross valdaton SD Average of Mn. MSEs Average of Fnal MSEs SD: Standard Devaton Table 3: The mnmum MSE and fnal MSE of SVM network Best networks Tranng Cross valdaton Epoch # Mnmum MSE Fnal MSE Fg. 6: Desred output and actual MLP network output Fg. 7: MSE of SVM versus epoch cross valdaton data sets. The practcal mplementaton of MLP s acheved a fnal MSE of ( ) n the tranng phase and t s acheved ( ) n cross valdaton phase. The fnal MSE of cross valdaton wth standard devaton s ( ). Table 2 llustrates Average of Fnal MSEs of tranng phase, cross valdaton phase and cross valdaton wth standard devaton results. The experment s gvng evdence that there s a strong relaton between the nput and the output varables. The correlaton factor (r) of MLP network s ( ). Fgure 5 depcts the lne graph the average MSE of fnal MLP wth standard devaton boundares for three runs. Whle Fg. 6 depcts the graph of the average MSE of the desred current PV output and the MLP network output, whch s predcate exactly the tested data. And then can be generalzed to utlze any unseen data for the current and future tme. Fgure 7 shows the tranng and cross valdaton MSE of the SVM network, whch s clearly demonstratng the graph lne of tranng data set n lne wth cross valdaton data sets. The practcal mplementaton of SVM s acheved a fnal MSE of ( ) n tranng phase and ( ) n cross valdaton phase as llustrated n Table 3. The experment s gvng evdence that there s a strong relaton between the nput and the output varables. The correlaton factor (r) of SVM network s ( ). Fgure 8 depcts the graph of comparson of desred PV output current and the network output, whch s predcate exactly the tested data. And then can be generalzed to utlze any unseen data for the current and future tme. SUMMARY AND CONCLUSION Fg. 8: Desred output and actual SVM network output 242 MLP and SVM networks and models for calculatng photovoltac energy generaton based on expermental work n Oman. The proposed models have been evaluated on the bass of Mean Square Error. Table 4 llustrates the comparson of fnal MSE between the MLP and SVM networks, whch s clearly ndcate that the MLP network acheved very good result n comparson to SVM results. However, the MSE of MLP s ( ), whle Fnal MSE of SVM s ( ). As a concluson the results showed that MLP model s more accurate than SVM model n calculatng the photovoltac generated energy n Oman.

7 Table 4: The comparson of fnal MSE between the proposed MLP and SVM networks Best networks Tranng Cross valdaton Epoch # Fnal MSE of MLP Fnal MSE of SVM The practcal experments are acheved n the predctng model a value of (R² = ) for SVM and (R² = ) for MLP whch ndcates the predctng models are very close to the regresson lne and a well data fttng to the statstcal models. Besdes, the proposed models acheved less MSE n comparson wth other related work. The predctng model for SVM network output s generated by a polynomal of forth orders as defned n Eq. (7): y = x x x x (7) Whle, the predctng model for MLP network output s generated by a logarthmc as defned n Eq. (8): y = ln(x)+2421 (8) ACKNOWLEDGMENT The research leadng to these results has receved Research Project Grant Fundng from the Research Councl of the Sultanate of Oman, Research Grant Agreement No. ORG SU EI The authors would lke to acknowledge support from the Research Councl of Oman. REFERENCES Altunkaynak, A. and M. Özger, Temporal sgnfcant wave heght estmaton from wnd speed by perceptron Kalman flterng. Ocean Eng., 31(10): Asl, S.F.Z., A. Karam, G. Ashar, A. Behrang, A. Assareh and N. Hedayat, Daly global solar radaton modelng usng Mult-Layer Perceptron (MLP) neural networks. World Acad. Sc. Eng. Technol., 55: Authorty for Electrcty Regulaton, Oman, Study on renewable energy resources, Oman. Fnal Report, Preparedby Cow and Partners, Muscat, Oman, pp: 134. Banda, D., R. Peña, G. Gutérrez, E. Juárez, N. Vsaro and C. Núñez, Feasblty assessment of the nstallaton of a photovoltac system as a battery chargng center n a mexcan mnng company. Proceedng of the IEEE Internatonal Autumn Meetng on Power, Electroncs and Computng (ROPEC, 2014). Ixtapa, pp: 1-5. Beyer, H.G. and H.P. Schwefel, Evoluton strateges - A comprehensve ntroducton. Nat. Comput., 1(1): Res. J. Appl. Sc. Eng. Technol., 13(3): , Caudll, M. and C. Butler, Understandng Neural Networks: Computer Exploratons Volume 1: Basc Networks. The MIT Press, Cambrdge, Mass, USA. Chaabene, M. and M. Ben Ammar, Neuro-fuzzy dynamc model wth Kalman flter to forecast rradance and temperature for solar energy systems. Renew. Energ., 33(7): Chen, J.L., H.B. Lu, W. Wu and D.T. Xe, Estmaton of monthly solar radaton from measured temperatures usng support vector machnes A case study. Renew. Energ., 36(1): Da, Y., B. Chakraborty and M. Sh, Kanse Engneerng and Soft Computng: Theory and Practce. IGI Global, Hershey, New York, pp: Elmnr, H.K., Y.A. Azzam and F.I. Younes, Predcton of hourly and daly dffuse fracton usng neural network, as compared to lnear regresson models. Energy, 32(8): Fakham, H., D. Lu and B. Francos, Power control desgn of a battery charger n a hybrd actve PV generator for load-followng applcatons. IEEE T. Ind. Electron., 58(1): Hontora, L., J. Agulera and P. Zufra, An applcaton of the multlayer perceptron: Solar radaton maps n Span. Sol. Energy, 79(5): Hossan, M.R., A.M.T. Oo and A.B.M. Shawkat Al, Hybrd predcton method of solar power usng dfferent computatonal ntellgence algorthms. Proceedng of the 22nd Australasan Unverstes Power Engneerng Conference. Bal, pp: 1-6. Jang, Y., Predcton of monthly mean daly dffuse solar radaton usng artfcal neural networks and comparson wth other emprcal models. Energ. Polcy, 36(10): Kazem, H.A., Renewable energy n Oman: Status and future prospects. Renew. Sust. Energ. Rev., 15(8): Kazem, H.A., R. Abdulla, F. Hason and A.H. Al-Wael, Prospects of potental renewable and clean energy n Oman. Int. J. Electron. Comput. Commun. Technol., 1(2): Kazem, H.A., T. Khatb and K. Sopan, Szng of a standalone photovoltac/battery system at mnmum cost for remote housng electrfcaton n Sohar, Oman. Energ. Buldngs, 61: Kazem, H.A., T. Khatb, K. Sopan and W. Elmenrech, Performance and feasblty assessment of a 1.4 kw roof top grd-connected photovoltac power system under desertc weather condtons. Energ. Buldngs, 82:

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