Abstract
The Fuzzy regression model provides a good alternative to the standard regression model that existing in statistics as well as engineering based studies. In this study, a new fuzzy regression model is introduced by incorporating the crisp and the spreading for the fuzziness of the data. The fuzzy triangular number is employed to obtain the fuzzy regression equation, i.e. left and right fuzzy quadratic regression model. This model will be used to predict the amount of solar radiation received at Universiti Teknologi PETRONAS (UTP), Malaysia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Karim SAA, Singh BSM, Razali R, Yahya N (2011) Data compression technique for modeling of global solar radiation. In: Proceeding of 2011 IEEE international conference on control system, computing and engineering (ICCSCE) 25–27 Nov 2011, Holiday Inn, Penang, pp 448–352
Karim SAA, Singh BSM, Razali R, Yahya N, Karim BA (2011) Solar radiation data analysis by using Daubechies wavelets. In: Proceeding of 2011 IEEE international conference on control system, computing and engineering (ICCSCE) 25–27 Nov 2011, Holiday Inn, Penang, pp 571–574
Karim SAA, Singh BSM, Razali R, Yahya N, Karim BA (2011) Compression solar radiation data using Haar and Daubechies wavelets. In: Proceeding of regional symposium on engineering and technology 2011, Kuching, Sarawak, Malaysia, 21–23 Nov 2011, pp 168–174
Karim SAA, Singh BSM (2013) Global solar radiation modeling using polynomial fitting. Appl Math Sci 8:367–378
Karim SAA, Singh BSM, Karim BA, Hasan MK, Sulaiman J, Janier Josefina B, Ismail MT (2012) Denoising solar radiation data using Meyer wavelets. AIP Conf Proc 1482:685–690. https://doi.org/10.1063/1.4757559
Jalil MAA, Karim SAA, Baharuddin Z, Abdullah MF, Othman M (2018) Forecasting solar radiation data using Gaussian and polynomial fitting methods. In: Sulaiman SA, Kannan R, Karim SAA, Nor NM (eds) Sustainable electrical power resources through energy optimization and future engineering. Springer Briefs in Energy. Springer Nature Singapore Pte. Ltd.
Khatib T, Mohamed A, Sopian K (2012) A review of solar energy modeling techniques. Renew Sustain Energy Rev 16:2864–2869
Sulaiman MY, Hlaing Oo WM, Wahab AM, Sulaiman MZ (1997) Analysis of residuals in daily solar radiation time series. Renew Energy 29:1147–1160
Wu J, Chan CK (2011) Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Sol Energy 85:808–817
Wang Y (2012) Statistics & applied probability. In: Smoothing splines: methods and applications. Chapman and Hall/CRC
Hansen PC, Pereyra V, Scherer G (2012) Least squares data fitting with applications. The Johns Hopkins University Press
Isa NHM, Othman M, Karim SAA (2018) Multivariate matrix for fuzzy linear regression model to analyze the taxation in Malaysia. Int J Eng Technol 7(4.33):78–82
Pan NF (2008) Fuzzy AHP approach for selecting the suitable bridge construction method. Autom Constr 17:958–965
Pan NF, Lin TC, Pan NH (2009) Estimating bridge performance based on a matrix-driven fuzzy linear regression model. Autom Constr 18:578–586
Xiao M, Li C (2018) Fuzzy regression prediction and application based on multi-dimensional factors of freight volume. IOP Conf Ser Earth Environ Sci 108:032071. https://doi.org/10.1088/1755-1315/108/3/032071
Acknowledgements
This study is fully supported by Universitas Islam Riau (UIR), Pekanbaru, Indonesia and Universiti Teknologi PETRONAS (UTP), Malaysia through International Collaborative Research Funding (ICRF): 015ME0-037. The first author is currently doing his internship at UTP under Research Attachment Program (RAP).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Yasin, M.I., Karim, S.A.A., Ismail, M.T., Skala, V. (2020). Fuzzy Regression Model to Predict Daily Global Solar Radiation. In: Karim, S., Abdullah, M., Kannan, R. (eds) Practical Examples of Energy Optimization Models. SpringerBriefs in Energy. Springer, Singapore. https://doi.org/10.1007/978-981-15-2199-7_1
Download citation
DOI: https://doi.org/10.1007/978-981-15-2199-7_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2198-0
Online ISBN: 978-981-15-2199-7
eBook Packages: EnergyEnergy (R0)