Model for Estimation of Global Solar Radiation in Sarawak, Malaysia

Transcription

1 World Applied Sciences Journal 14 (Special Issue of Food and Environment): 83-90, 011 ISSN IDOSI Publications, 011 Model for Estimation of Global Solar Radiation in Sarawak, Malaysia A.Q. Jakhrani, A.K. Oman, A.R.H. Rigit and S.R. Samo 1 Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia Department of Energy and Environment Engineering, Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Sindh, Pakistan Abstract: The aim of is work was to estimate e obtainable amount of global solar radiation for e development of solar photovoltaic power systems at four locations namely Sri Aman, Sibu, Bintulu and Limbang in Sarawak, Malaysia. For is purpose, ree empirical models namely Angstrom type Page model, Hargreaves equation and e Sayigh Universal formula were selected for e assessment of eir estimated values. The data of selected locations were acquired from e Malaysian Meteorological Services Regional Office based at Kuching. It is confirmed from e evaluation of results at e selected models overestimated e amount of obtainable global solar radiation when compared wi e measured data taken from e nearest station. The models did not perform well in almost all four selected places. This may be due to e weaer conditions and geographical location of e area. Therefore, an improved model was formulated based on e recorded meteorological data of available parameters such as bright sunshine hours, relative humidity and maximum temperature. The proposed model established less an 10 and 8 percentage of root mean square error and mean bias error and produced acceptable results as compared to examined models. Therefore it is preferred for e estimation of global solar radiation in e remote places of Sarawak, where e facilities for e measurement of global solar radiation by pyranometers is not available. Key words: Bright sunshine hours Empirical models Global solar radiation Meteorological parameters Solar energy systems INTRODUCTION monly average values of daily irradiation, are usually e best source of information and provide e starting Solar energy is a natural green technology resource, point for many calculations. The usefulness of long-term environment and nature-friendly, does not produce monly averages of daily radiation can be understood emissions at contribute greenhouse effect or destroy from e fact at at a particular location ese averages e ecological balance of e area and occupies one of e are relatively constant, so at past values can be used to most important places among various alternative energy estimate future ones. If e recorded solar radiation data sources [1]. Information of solar radiation data is a pre- is not available, en ere is one possibility to get requisite for e modeling and design of all solar energy reasonable estimation of solar radiation by using systems. Architects, agriculturalists, air conditioning commonly available meteorological parameters, such as engineers and energy designers require e familiarity of bright sunshine hours, relative humidity, maximum and solar radiation data [-8], for climatology and pollution minimum temperatures and cloud cover of at geographic studies [9-10] and for many oer applications, such as location [14]. For is purpose, in e past, numerous atmospheric energy-balance studies, analysis of e empirical correlations have been developed in order to ermal load on buildings, solar energy collecting systems estimate e available global solar radiation around e and economic viability of meods [11, 1]. The network world. Several models are existed, which correlate e of meteorological stations measuring e solar radiation global solar radiation to oer meteorological parameters data is sparse in many developing countries and only few such as bright sunshine hours, relative humidity and stations are well equipped for e measurement of e maximum temperature. The most commonly used daily solar radiation on a consistent basis [13]. Measured parameter for estimating global solar radiation, is bright Corresponding Auor: Abdul Qayoom Jakhrani, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia. Tel: , Fax:

2 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, 011 sunshine duration. The sunshine duration can be easily The recommended values for e constants a = 0.8 and and reliably measured and e data is also widely available b = 0.39 (for tropical forest climate, constant moisture, in most of places around e globe [15]. The data of rainfall roughout e year and for broadleaf evergreen sunshine hours and oer parameters requires a trees) were used for e selected locations. correlation for estimation of available global solar radiation. It can be carried out by e use of empirical Hargreaves Equation: The first idea for e calculation of models. Empirical Models were found to be essential and clearness index from e difference of e mean value of economical tools for transforming e meteorological e daily maximum temperatures (T max) and minimum parameters to calculate e amount of global solar temperatures (T min) was proposed by Hargreaves (198). radiation. The equation is given as [18]: The basic and fundamental equation has been used since 194 is Angstrom-type regression model [16]. Most H = 0.5 H o (T d) () auors modified at equation for e prediction of solar radiation data. Moreover, oers formulated quadratic, Where, T d is e daily temperature difference maximum linear, logarimic, power and trigonometric models. In minus minimum ( C). The value of empirical parameter a is study, ree empirical models were examined for was initially fixed as 0.17 for arid and semiarid climates. computing global s olar radiation at various places of Later, Hargreaves recommended e use of a = 0.16 for Sarawak State namely; Sri Aman (1.14 N, E), Sibu interior regions and a = 0.19 for coastal regions [19-1]. (.17 N, E), Bintulu (3.13 N, E) and Limbang The temperature difference becomes less when cloud (4.53 N, E). The first empirical relationship used for cover is greater. This is because, e day temperatures analysis, was proposed by Page, which was based on remain high and e heat is conserved so at e night data of bright sunshine hours and climatic constants. The temperature is also high, resulting in less temperature second equation given by Hargreaves which employed range during e day. The temperature difference takes temperature difference (maximum and minimum) and e into account e changes in e solar radiation due to ird formula involved e parameters such as relative proximity to oceans, mountains and e altitude of e humidity, bright sunshine hours, ambient temperature and location []. The data for Hargreaves equation is mostly sun angles was developed by Sayigh. The brief available at many locations around e world. introduction of e selected empirical models is given below: Sayigh Universal Formula: The Sayigh Universal formula correlated e global solar radiation (H) wi mean Angstrom Type Page Model: Page (1964) incorporated e maximum temperature T max ( C), mean relative humidity RH term of extraterrestrial radiation on a horizontal surface for (taken as a fraction) and e ratio of sunshine hours to e clarification of term clear day radiation used by Angstrom leng of e day [3-9]. It is expressed as: [17]. The model is given as: RH 1 H = k Kexp S H = H [a + b(s)] (1) 15 Tmax o (3) k = (4) Where, H o is e extraterrestrial radiation on a horizontal surface for e location averaged over e time period in a = 0./(1+0.1 ) (5) question and a and b are constants depending on location and S is considered as e ratio of monly k and a are constants, is e latitude of e location, average bright sunshine hours ( n ) to e day leng ( N) (taken in radians for k & H and employed for e hours, of e average day of e mon. The monly average daily extraterrestrial solar irradiance on e horizontal surface was computed by taking e values of a single day (close to monly mean values) for every mon of e year. The value of constants (a) and (b) were developed for various climate types and locations, at can be used in e absence of available constants for any area wi similar meteorological conditions [15]. calculation in degrees for a and K). ( ij ) K = a N + cos Where, K is a constant (kwh/m /day), while k, and K are factors which were computed using e respective equations. ij is a humidity factor, i = 1,,3. The value of 1 is used for RH < 65%, is for RH > 70% and 3 is for 65% (6) 84

3 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, 011 RH 70%. On e oer hand, j = 1,, 1, refers to T H H max = o a+ ( S RH ) (7) e mons of e year. Humidity factor ij was taken from e Figure provided by [9]. Where, a is e location constant depending on geographical site and environmental conditions of e Formulated Model: The Sayigh Universal Formula could area. The value of a was calculated by collecting e not be easily implemented because of many variables information for e mean ambient temperature, wind involved in e model. The Hargreaves equation speed, cloud cover, rainfall conditions, vegetation types considered only temperature difference and did not and pollution level of e area as expressed in equation (8) account any climatic factor. The constant or interpolated values were used in Page model, which do not varies Number of Parameters a = according to e environmental conditions. But, e Sumof Assigned Values (8) proposed model incorporates e environmental aspects The parameters, eir description and associated such as temperature and humidity. These factors were assigned values are given in Table 1. The assigned values considered as changing forces at affect e amount of will be chosen from e corresponding ranges of global radiation received at any particular place. The geographical and environmental parameters of e area. criteria framed for is approach was based on simplicity, The value of a was found to be 0.4 for Sarawak region as accuracy and availability of input data. It was assumed per formulated procedure. T max is e average maximum at e suggested model meets all ese requirements. temperature ( C) and RH is relative humidity taken as The model is expressed as follows: whole not in fraction. For example: If e RH is 75%, en Table 1: Description for e Determination of Location Constant a S.R. No Location and Environmental Parameters Description and Range of Parameters Assigned Values 1 Mean Ambient Temperature ( C) Wind Speed (m/s) < > Cloud Cover (Oktas) Rainfall Conditions No or light rainfall 1 Low rainfall Rainfall roughout year wi long dry season 3 Rainfall roughout year wi short dry season 4 Rainfall roughout year 5 5 Condition of Vegetation Desert 1 Grasses and herbaceous plants Broadleaf evergreen trees shrub form 3 Broadleaf deciduous trees 4 Broadleaf evergreen trees 5 6 Pollution Level (Particulate Matter) Extremely high 1 High Medium 3 Low 4 None or Negligible 5 85

4 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, 011 it is taken as 75 in equation (7). In proposed model, e corresponding values were also calculated for e temperature and relative humidity were related wi e comparison of computed results wi measured values bright sunshine hours, which take into account e [4]. These statistical meods are defined by e changeable nature of variables at restricts e amount following equations [5-6]: of global solar radiation reaching to e ear s surface. RMSE = ( yi yˆ i) (9) n MATERIALS AND METHODS ( ˆ ) Data Acquisition: The data was obtained from Malaysian MBE = yi yi n (10) Meteorological Services, Regional Office Kuching. The facility for measuring e solar radiation data is available at Kuching only in e whole Sarawak State. Since, six % RMSE = RMSE 100 (11) y years measured global solar radiation data for e year from Kuching was used for e comparison of estimated results computed from selected models. The MBE % MBE = 100 (1) 10 years data of sunshine hours, relative humidity, y monly mean, maximum and minimum air temperature Where, y i is e i calculated value, ŷi is e i measured value, y is e mean of e measured values and n is e number of valid data points. from was used for ree stations, namely Sri Aman, Sibu and Bintulu. Only 3 years data from 007 to 009 was used for Limbang. The previous data was not available for at location. The daily meteorological variables were e maximum and minimum temperatures and bright sunshine hours and relative humidity. Single day for each mon was selected for representing e entire mon for e whole year as suggested by Klein (1977). The 17 of January and July, 16 of February, March and August, 15 of April, May, September and October, 14 November, 11 of June and 10 December were chosen days for is analysis [16]. The selected days gave results close to e monly mean values for every mon of e year, representing at individual mon. Then, declination, sunset hour angle, average day leng were computed by empirical relationships. After at, e monly average daily extraterrestrial solar irradiance (H o) on e horizontal surface was determined by taking e average values of a single day for all mons. Three models were examined for e determination of global solar radiation at four sites of Sarawak State. Finally, e results were compared wi global solar radiation data which is measured at Kuching, e nearest station wi similar meteorological conditions. Accuracy and Validation: The accuracy of different models and correlations was evaluated by means of two widely used statistical meods, root mean square error (RMSE) and mean bias error (MBE). In general, a low RMSE is desirable, e positive MBE demonstrates at e predicted values overestimated e measured values; whereas, a negative MBE indicates e underestimation of e observed values. The percentage RMSE and MBE of RESULTS AND DISCUSSION The estimated global solar radiation of all four selected locations namely Sri Aman, Sibu, Bintulu and Limbang are shown in Figures 1 to 4. Sayigh Universal Formula predicted nearly 30 MJ/m from e mon of April to July in Sri Aman and Sibu as shown in Figures 1 and. Hargreaves equation estimated between 3 and 6 MJ/m for e entire year in bo places. The results obtained from Angstrom type Page model were 15 to 16MJ/m, which were found to be closest values to e measured ones. The Sayigh formula predicted lower solar radiation availability in e mons of November and December an Hargreaves equation for Sri Aman and Sibu and from January to March for Limbang. This is because ere is less temperature variation in tropical regions. The less variation has a little effect on e estimation of global solar radiation. Sayigh Universal formula estimated higher values of available global solar radiation in dry season from April to September in all locations among e examined models. Whereas, e estimated values of Hargreaves equation were lower an Sayigh Universal formula in dry season and higher in wet season. As compared to oer examined models Angstrom type Page model displayed quite reasonable results in dry as well as wet season for all mons at every selected location, because it incorporates e climatic and location coefficients. The obtained results of Angstrom type Page model were between 13 and 18MJ/m in all observed 86

5 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, 011 Fig. 1: Values of monly mean daily global solar radiation verses time at Sri Aman Fig. : Values of monly mean daily global solar radiation verses time at Sibu Fig. 3: Values of monly mean daily global solar radiation verses time at Bintulu locations. These results were found to be in agreement wi measured values of e area. Higher prediction of available solar radiation by Sayigh Universal formula and Hargreaves equation was unjustifiable due to e presence of dense clouds and high humidity. The prediction of Angstrom type Page model was quite acceptable. The accuracy of calculated data was checked by estimating RMSE and MBE values and eir respective percentage error for all models, which are graphically presented in Figures 5 and 6. The analysis of entries confirmed at e Hargreaves and Sayigh models are not feasible for Sarawak climatic conditions due to extremely high RMSE and MBE and eir models overestimated e 87

6 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, 011 Fig. 4: Values of monly mean daily global solar radiation verses time at Limbang Fig. 5: Comparison of RMSE and MBE in (MJ/m ) of examined models verses locations Fig. 6: Comparison of Percentage RMSE and MBE of examined models verses locations amount of global radiation of e area, whereas, e It was revealed at e Hargreaves equation Angstrom type Page model showed 0 percentage error. demonstrated high values in all mons, whereas; The The formulated model performs well in e Sarawak Sayigh's Formula established nearly two times higher scenario and demonstrated less an 10 and 8% of values an measured results at nearest location, percentage RMSE and MBE respectively. The RMSE and especially from e mon of March to October. Angstrom MBE were demonstrated higher at Limbang only, in e type Page model exhibited better and reliable results as proposed model. It is because e long term data was not compared to oer models and slightly overestimated e available in at location and only ree years average results in e mon of January to March and from data was used. At oer stations e results were found October to December at all places, except at Limbang very close to unity. where it demonstrated higher values in e whole year. 88

7 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, 011 Usually, e Hargreaves' equation and Sayigh's Universal 6. Chang, J.M., J.S. Leu, M.C. Shen and B.J. Huang, Formula does not perform well in Sarawak region, it may 004. A Proposed Modified Efficiency for be due to e high relative humidity factor in e area and Thermosyphon Solar Heating Systems. Solar Energy, less temperature difference. These equations may be valid 76(6): for dry and temperate climate regions, but not useful in 7. Ghosal, M.K., G.N. Tiwari, D.K. Das and K.P. Pandey, is region. On e oer hand, e proposed model 005. Modeling and Comparative Thermal showed satisfactory results as compared to reviewed Performance of Greenhouse Air Collector and Ear models and its values were found more convincing in all Air Heat Exchanger for Heating of Greenhouse. stations of Sarawak State. Energy and Buildings, 37(6): Tiwari, G.N., H.N. Singh and R. Tripai, 003. CONCLUSIONS Present Status of Solar Distillation. Solar Energy, 75(5): It was found at Hargreaves' equation and Sayigh's 9. Gueymard, C. and F. Vignola, Determination of Universal Formula overestimated e available amount of Atmospheric Turbidity from e Diffuse-Beam global solar radiation and were not suitable estimators for Broadband Irradiance Ratio. Solar Energy, e monly mean global solar radiation in e region. The 63(3): Angstrom type Page model produced appropriate results 10. Kharol, S.K., K.V.S. Badarina, D.G. Kaskaoutis and of global solar radiation wi a 0 percentage error as H.D. Kambezidis, 007. Impact of Dust Storm Over compared to e measured data. Moreover, no agreement Indian Region on Ground Reaching Solar Radiation; was found in e obtained results of reviewed models A Case Study Using Multi-Satellite Data and Ground when compared among each oer and wi measured rd Measurements. In Proceedings of 3 International data taken from e nearest station at Kuching. Conference on Solar Radiation and Day Lighting, The formulated model demonstrated less an 10 and Indian Institute of Technology Delhi, New Delhi, 8 percentage of root mean square error and mean bias India, pp: error. Therefore it is preferred for e estimation of global 11. Metwally, M.E., 005. Sunshine and Global Solar solar radiation in e remote places of Sarawak, where e Radiation Estimation at Different Sites in Egypt. J. facility for e measurement of global solar radiation by Atmospheric and Solar-Terrestrial Physics, 67: 134. pyranometers is not available. 1. Robaa, S.M., 008. Evaluation of Sunshine Duration from Cloud Data in Egypt. Energy, 33: REFERENCES 13 Chineke, T.C., 008. Equations for Estimating Global Solar Radiation in Data Sparse Regions. Renewable 1. Jakhrani, A.Q., A.K. Oman, A.R.H. Rigit, S.R. Samo, Energy, 33(4): L.P. Ling and R. Baini, 010. Evaluation of Incident 14. Suehrcke, H., 000. On e relationship between Solar Radiation on Inclined Plane by Empirical duration of Sunshine and Solar Radiation on e Models at Kuching, Sarawak, Malaysia, presented at Ear s Surface: Angstrom s Equation Revisited. e IEEE ICSET 010, Kandy, Sri Lanka. Solar Energy, 68(5): Muneer, T., S. Younes and S. Munawwar, Bakirci, K., 009. Models of Solar Radiation wi Discourses on Solar Radiation Modeling. Renewable Hours of Bright Sunshine: A Review. Renewable and and Sustainable Energy Reviews, 11: Sustainable Energy Reviews, 13: Helen C., 001. Power Estimating Clear-Sky Beam 16. Klein, S.A., Calculation of Monly Average Irradiation from Sunshine Duration. Solar Energy, Insolation on Tilted Surfaces, Solar Energy, 19: (4): Page, J.K., The Estimation of Monly Mean 4. Driesse, A. and D. Thevenard, 00. Test of Values of Daily Total Short Wave Radiation on Suehrcke s Sunshine-Radiation Relationship using a Vertical and Inclined Surface from Sunshine Records Global Data Set. Solar Energy, 7(): for Latitudes 40N-40S. Proceedings of UN Conference 5. Li, D.H.W., T.N.T Lam and V.W.C. Chu, 008. on New Sources of Energy, pp: Relationship between e Total Solar Radiation on 18. Hargreaves, G.H. and Z.A. Samani, 198. Estimating Tilted Surfaces and e Sunshine Hours in Hong Potential Evapo-transpiration. J. Irrg. Drain. Engrg, Kong. Solar Energy, 8(1): (3):

8 World Appl. Sci. J., 14 (Special Issue of Food and Environment): 83-90, Prieto, J.I., J.C.M. Garcia and D. Garcia, 009, 5. Pandey, C.K. and A.K. Katiyar, 009. A Note on Correlation between Global Solar Irradiation and Air Diffuse Solar Radiation on a Tilted Surface. Energy, Temperature in Asturias, Spain. Solar Energy, 34: : Efim, G.E. and A.I. Kudish, 009. The Assessment of 0. Hargreaves, G.H., Simplified Coefficients for Different Models to Predict e Global Solar Estimating Monly Solar Radiation in Nor America Radiation on a Surface Tilted to The Sou. Solar and Europe. Departmental Paper, Dept.of Biol. and Energy, 83: Irrig. Engrg. Utah State University, Logan, Utah. 7. Lof, G.O.G., J.A. Duffie and C.O. Smi, Chineke, T.C., J.I. Aina and S.S. Jagtap, Engineering Experiment Station Report 1, University Solar radiation Database for Nigeria. Discov Innov., of Wisconsin, Madison, World distribution of Solar 11: Radiation.. Halawa, E.E.H. and Sugiyatno, 001. Estimation of 8. Duffie, J.A. and W.A. Beckman, 006. Solar Global Solar Radiation in e Indonesian Climatic rd Engineering of Thermal Processes. 3 Edition, John Region. Renewable Energy, 4: Wiley & Sons Inc. 3. Sayigh, A.A.M., Estimation of Total Radiation 9. Afriyie, K.O., International Centre for Intensity-A Universal Formula. In N.A. Mancini and Theoretical Physics Performance of Sayigh's I.F. Quercia, (Eds.) 4 Course on Solar Energy Universal Formula in e Estimation of Global Solar Conversion, : ICTP, Trieste, Italy. Radiation in Ghana. International Atomic Energy 4. De Miguel, A., J. Bilbao, R. Aguiar, H. Kambezidis Agency and United Nations Educational Scientific and E. Negro, 001. Diffuse Solar Irradiation Model and Cultural Organization. Evaluation in e Nor Mediterranean Belt Area. Solar Energy, 70():