THE PREDICTION OF SOLAR RADIATION USING FUZZY LOGIC: A CASE STUDY

  • JWAN ABDULKHALIQ MOHAMMED Dept. of Computer Science, College of Science, University of Duhok, Kurdistan Region-Iraq
  • BERIVAN HADI MAHDI Branch of Basic Science, College of Agriculture, University of Duhok, Kurdistan Region-Iraq
Keywords: Fuzzy logic;, Solar radiation;, Prediction;, Membership functions ;

Abstract

Solar energy is used in many applications such as producing agricultural food, renewable energy, and heating and lighting systems… etc. Nowadays, countries all over the world, especially the developing countries are facing a great challenge which is providing sustainable energy for consumers. Electricity is the most common type of energy that is used by consumers in which oil or nuclear power is used to produce sufficient amount of electricity for the constant increase of the population in the present. However, both oil and nuclear energy negatively affect the global warming; therefore, solar energy is aspired by many countries to decrease the effects of the global warming and produce renewable sources of energy. The aim of this study is to predict the use of solar radiation for solar energy to produce electricity in Duhok city due to the fact that “national electricity” is not enough for the great number of consumers; as a result, people depend on “local or private generators” which mainly depend on oil to produce electricity. Fuzzy logic approach is used to estimate the solar radiation. The four fuzzy systems are created using the available data in Duhok City in 2016. Daily observations for temperature, humidity and wind speed for four seasons are analyzed to estimate the solar radiation. The predicted outputs of fuzzy logic system are compared with the actual solar radiation. In addition, the fuzzy system approach is evaluated using Mean Absolute Percentage Error (MAPE) and Absolute Percentage Error (APE). The outcomes of MAPE and APE are 5.86%, 1.54%, 2.76% and 1.52 for four seasons (winter, summer, spring and fall), respectively. According to the results, the performance of fuzzy system is reasonably effective in predicting the solar radiation.

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References

Ali, D., Yohanna, M., Puwu, M.I., and Garkida, B.M. (2016). Long-term load forecast modelling using a fuzzy logic approach. Pacific Science Review A: Natural Science and Engineering, 18(2), 123-127.
Bauer, I. (2018). Framing, overflowing, and fuzzy logic in educational selection: Zurich as a case study. Geogr. Helv., 73, 19-30.
Bunnoon, P. (2011). Mid-Term Load Forecasting Based on Neural Network Algorithm: a Comparison of Models. International Journal of Computer and Electrical Engineering, 3(4), 600-605.
Coops, N. C., Waring, R. H. and Moncrieff, J. B. (2000). Estimating Mean Monthly Incident Solar Radiation on Horizontal and Inclined Slopes from Mean Monthly Temperatures Extremes. International Journal of Biometeorology, 44, 204–211.
Cordón, O., Herrera, F., and Villar, P. (2001). Generating the knowledge base of a fuzzy rule-based system by the genetic learning of data base. IEEE Trans Fuzzy Systems, 9, 103-108.
Cornelis C, Kerre E (2003) A fuzzy inference methodology based on the fuzzification of set inclusion. In: Recent advances in intelligent paradigms and applications, Physica-Verlag, 71–89
Fan, S., Methaprayon, K., and Lee, W.J. (2009). Multi-Region Load Forecasting Based on Weather and Load Diversity Analysis. IEEE Trans. Ind. Appl. 45(4), 1452-1459,
Filik ,U.B., Gerek ,O.N., and Kurban, M. (2011). Hourly forecasting of long term electric energy demand using novel mathematical models and neural networks. International journal of innovative computing, information and control, 7, 3545–3557.
Hong, T., and Lee, C.(1996). Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets System, 84(1), 33-37.
Ibrahim, H.A. (2003). Fuzzy Logic for Embedded Systems Applications. USA: Butterworth-Heinemann.
Ismail ,Z., Mansor, R, and Ta'zim, J.D. (2011). Fuzzy Logic Approach for Forecasting Half-hourly.
Jantakoon, N. (2016). Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model. Mathematics and Statistics, 4(4) , 95 – 100.
Jeon, G., Anisetti, M., Lee, J., Bellandi, V., Damiani,E, and Jeong, J.(2009). Concept of linguistic variable-based fuzzy ensemble approach: application to interlaced HDTV sequences. IEEE Transactions on Fuzzy Systems, 17(6), 1245-1258.
Karwade, S.B. and Ali, M.S.(2015). Review Paper on Load Forecasting Using Neuro Fuzzy System. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 10(3), 38-42.
Mordjaoui, M., Boudjema, B., Bouabaz, M., and Radouane, D.(2010). Short term electric load forecasting using Neuro-fuzzy modeling for nonlinear system identification. LRPCSI Laboratory, University of 20August, Skikda, 5,1-6.
Pujar, J.H. (2010). Fuzzy Ideology based Long Term Load Forecasting. International Journal of Computer and Information Engineering, 4(4), 790-795.
Ronald, C.P., and Sari, S.R. (2014). Long Term Load Forecasting in Tamil Nadu Using Fuzzy-Neural Technology. International Journal of Engineering and Innovative Technology (IJEIT), 3(9), 5-8.
Surmann, H. and Selenschtschikow, A. (2002). Automatic generation of fuzzy logic rule bases Examples I. In Proc. of the NF 2002: first international ICSC conference on Neuro-Fuzzy Technologies. Cuba, 16-19 January, 75-81.
Swanson, D.A., Tayman, J., and Bryan, T. (2011). Mape-r: a rescaled measure of accuracy for cross-sectional subnational population forecasts. J Popul Res, 28, 225-243.
Swaroop, R., and Abdulqader, H.A.(2012). Load forecasting for power system planning using fuzzy-neural networks. In Proceedings of the World Congress on Engineering and Computer Science, USA. 1, 1-5.
Yazdanbakhsh O. and Dick, S. (2017). A systematic review of complex fuzzy sets and logic. Fuzzy Sets Syst,1,1-22.
Yazdani, M.G., Salam, M.A. and Q.M. Rahman (2014). Investigation of the effect of weather conditions on solar radiation in Brunei Darussalam. International Journal of Sustainable Energy, DOI:10.1080/14786451.2014.969266.
Zadeh, L.A. (1965). Fuzzy Sets. Information and control, 8, 338—353.
Published
2019-07-01
How to Cite
MOHAMMED, J. A., & MAHDI, B. H. (2019). THE PREDICTION OF SOLAR RADIATION USING FUZZY LOGIC: A CASE STUDY. Journal of Duhok University, 21(2), 34-44. https://doi.org/10.26682/sjuod.2018.21.2.4
Section
Pure and Engineering Sciences