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Fish swarmed Fuzzy Time Series for Photovoltaic’s Forecasting in Microgrid
Corresponding Author(s) : Fitri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 3, August 2023
Abstract
Forecasting irradiation and temperature is important for designing photovoltaic systems because these two factors have a significant impact on system performance. Irradiation refers to the amount of solar radiation that reaches the earth's surface, and directly affects the amount of energy that can be generated by a photovoltaic system. Therefore, accurate irradiation forecasting is essential for estimating the amount of energy a photovoltaic system can produce, and can assist in determining the appropriate system size, configuration, and orientation to maximize energy output. Temperature also plays an important role in the performance of a photovoltaic system. With increasing temperature, the efficiency of the solar cell decreases, which means that the energy output of the system also decreases. Therefore, accurate temperature forecasts are essential for estimating system energy output, selecting suitable materials, and designing effective cooling systems to prevent overheating. In summary, forecasting irradiation and temperature is important for designing photovoltaic systems as it helps in determining suitable system size, configuration, orientation, material selection, and cooling system, which ultimately results in higher energy output and better system performance. In recent decades, many forecasting models have been built on the idea of fuzzy time series. There are several forecasting models proposed by integrating fuzzy time series with heuristic or evolutionary algorithms such as genetic algorithms, but the results are not satisfactory. To improve forecasting accuracy, a new hybrid forecasting model combines fish swarm optimization algorithm with fuzzy time series. The results of irradiance prediction/forecasting with the smallest error are using the type of Fuzzy Time Series prediction model optimized with FSOA with RMSE is 0.83832.
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- Kun-Huang Huarng. Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems 2001; 123(3), 387-394. https://doi.org/10.1016/S0165-0114(00)00057-9
- Erol Egrioglu, Cagdas Hakan Aladag, Ufuk Yolcu, Vedide R. Uslu, Murat A. Basaran. Finding an optimal interval length in high order fuzzy time series. Expert Systems with Applications. 2010; Vol 37 (7). https://doi.org/10.1016/j.eswa.2009.12.006
- Chen, S and Chung, N. Forecasting Enrollments of Students by Using Fuzzy Time Series and Genetic Algorithms. Information and Management Sciences. 2006; 17, (3), 1-17. http://dx.doi.org/10.1002/0470024569.ch1
- Lee, L.S. MultiCrossover Genetic Algorithms for Combinatorial Optimisation Problems. University of Southampton, United Kingdom. 2006.
- I-Hong Kuo, Shi-Jinn Horng, Tzong-Wann Kao, Tsung-Lieh Lin, Cheng-Ling Lee, Yi Pan. An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Systems with Applications. 2009; Vol 36(3). https://doi.org/10.1016/j.eswa.2008.07.043
- R.J. Kuo, S.Y. Hong, Y.C. Huang. Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Applied Mathematical Modelling. 2010. Vol 34(2). https://doi.org/10.1016/j.apm.2010.03.033
- Kuang Yu Huang. A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowledge-Based Systems. 2011. Vol 24(3). https://doi.org/10.1016/j.knosys.2010.12.003
- Cagdas Hakan Aladag, Erol Egrioglu, Cem Kadilar. Improvement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network. American Journal of Intelligent Systems. 2012; 2(2): 12-17.
- Cheng, C.-H., Chen, T., Teoh, H., & Chiang, C. Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert systems with applications. 2008;34.2 , 1126-1132. https://doi.org/10.1016/j.eswa.2006.12.021
- E. Egrioglu, C.H. Aladag, U. Yolcu, V.R. Uslu, N.A. Erilli. Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering. Expert Systems with Applications. 2011; 30(8). https://doi.org/10.1016/j.eswa.2011.02.052
- Yolcu, U., Egrioglu, E., & Aladag, C. H. A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 2013; 54(3), 1340–1347. https://doi.org/10.1016/j.dss.2012.12.006
- Musaed Alrashidi and Saifur Rahman, Short-term photovoltaic power production forecasting based on novel hybrid data-driven models. Journal of Big Data, 2023;10:26. https://doi.org/10.1186/s40537-023-00706-7
- Fran Sérgio Lobato, Valder Steffen Jr. Fish Swarm Optimization Algorithm Applied to Engineering System Design. Latin American Journal of Solids and Structures. 2014. 11, 143-156. https://doi.org/10.1590/S1679-78252014000100009
- Bogdan Oancea, Richard Pospíšil, Marius Nicolae Jula and Cosmin-Ionuț Imbrișcă. Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods. Mathematics 2021; 9, 2517. https://doi.org/10.3390/math9192517
- Rifandi, Angga Dwi Apria, Budi Darma Setiawan, Tibyani. Optimasi Interval Fuzzy Time Series Menggunakan Particle Swarm Optimization pada Peramalan Permintaan Darah : Studi Kasus Unit Transfusi Darah Cabang - PMI Kota Malang. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN: 2548-964X, Vol. 2, No. 7, Juli 2018, pg. 2770-2779.
- Sari, Ade Puspita. Optimasi Interval Fuzzy Time Series menggunakan Particle Swarm Optimization Untuk Memprediksi Kualitas Udara Di Kota Pekanbaru. Skripsi thesis 2019, Universitas Islam Negeri Sultan Syarif Kasim Riau.
- Bahrudin, Muhammad Irfan . Optimisasi Daya Photovoltaic pada kondisi Partially Shaded dengan Maximum Power Point Tracker (MPPT) Menggunakan Metode Particle Swarm Optimization (PSO) Double Diode Model. Institut Teknologi Sepuluh Nopember. 2018
- Tyas, F. A., Setianama, M., Fadilatul Fajriyah, R., & Ilham, A. (2021). Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(4). https://doi.org/10.22219/kinetik.v6i4.1330
- Noviandi, N., & Ilham, A. (2020). Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1). https://doi.org/10.22219/kinetik.v5i1.985
- Allen J. Wood, Power Generation Operation and Control, second edisi tahun 1996
- Frisk, M. (2017). Simulation and optimization of a hybrid renewable energy system for appli- cation on a cuban farm
- Saiprasad, N., Kalam, A., and Zayegh, A. (2018). Comparative study of optimization of hres using homer and ihoga software
- Akhter MN, Mekhilef S, Mokhlis H, Shah NM.(2019). Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew Power Gener. 2019;13(7):1009–23. https://doi.org/10.1049/iet-rpg.2018.5649
- Ahmed R, Sreeram V, Mishra Y, Arif D. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew Sustain Energy Rev. 2020;124:109792. https://doi.org/10.1016/j.rser.2020.109792
- Sharadga H, Hajimirza S, Balog RS.(2020). Time series forecasting of solar power generation for large-scale photovoltaic plants. Renew Energy. 2020;150:797–807. https://doi.org/10.1016/j.renene.2019.12.131
- Konstantinou M, Peratikou S, Charalambides AG.(2021). Solar photovoltaic forecasting of power outputusing LSTM net-works. Atmosphere. 2021;12(1):124. https://doi.org/10.3390/atmos12010124
- Miraftabzadeh SM, Longo M, Foiadelli F.(2020. A-day-ahead photovoltaic power prediction based on long short term memory algorithm. In: SEST 2020—3rd international conference on smart energy systems and technologies. 2020.p. 1–6. https://doi.org/10.1109/SEST48500.2020.9203481
References
Kun-Huang Huarng. Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems 2001; 123(3), 387-394. https://doi.org/10.1016/S0165-0114(00)00057-9
Erol Egrioglu, Cagdas Hakan Aladag, Ufuk Yolcu, Vedide R. Uslu, Murat A. Basaran. Finding an optimal interval length in high order fuzzy time series. Expert Systems with Applications. 2010; Vol 37 (7). https://doi.org/10.1016/j.eswa.2009.12.006
Chen, S and Chung, N. Forecasting Enrollments of Students by Using Fuzzy Time Series and Genetic Algorithms. Information and Management Sciences. 2006; 17, (3), 1-17. http://dx.doi.org/10.1002/0470024569.ch1
Lee, L.S. MultiCrossover Genetic Algorithms for Combinatorial Optimisation Problems. University of Southampton, United Kingdom. 2006.
I-Hong Kuo, Shi-Jinn Horng, Tzong-Wann Kao, Tsung-Lieh Lin, Cheng-Ling Lee, Yi Pan. An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Systems with Applications. 2009; Vol 36(3). https://doi.org/10.1016/j.eswa.2008.07.043
R.J. Kuo, S.Y. Hong, Y.C. Huang. Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Applied Mathematical Modelling. 2010. Vol 34(2). https://doi.org/10.1016/j.apm.2010.03.033
Kuang Yu Huang. A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowledge-Based Systems. 2011. Vol 24(3). https://doi.org/10.1016/j.knosys.2010.12.003
Cagdas Hakan Aladag, Erol Egrioglu, Cem Kadilar. Improvement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network. American Journal of Intelligent Systems. 2012; 2(2): 12-17.
Cheng, C.-H., Chen, T., Teoh, H., & Chiang, C. Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert systems with applications. 2008;34.2 , 1126-1132. https://doi.org/10.1016/j.eswa.2006.12.021
E. Egrioglu, C.H. Aladag, U. Yolcu, V.R. Uslu, N.A. Erilli. Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering. Expert Systems with Applications. 2011; 30(8). https://doi.org/10.1016/j.eswa.2011.02.052
Yolcu, U., Egrioglu, E., & Aladag, C. H. A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 2013; 54(3), 1340–1347. https://doi.org/10.1016/j.dss.2012.12.006
Musaed Alrashidi and Saifur Rahman, Short-term photovoltaic power production forecasting based on novel hybrid data-driven models. Journal of Big Data, 2023;10:26. https://doi.org/10.1186/s40537-023-00706-7
Fran Sérgio Lobato, Valder Steffen Jr. Fish Swarm Optimization Algorithm Applied to Engineering System Design. Latin American Journal of Solids and Structures. 2014. 11, 143-156. https://doi.org/10.1590/S1679-78252014000100009
Bogdan Oancea, Richard Pospíšil, Marius Nicolae Jula and Cosmin-Ionuț Imbrișcă. Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods. Mathematics 2021; 9, 2517. https://doi.org/10.3390/math9192517
Rifandi, Angga Dwi Apria, Budi Darma Setiawan, Tibyani. Optimasi Interval Fuzzy Time Series Menggunakan Particle Swarm Optimization pada Peramalan Permintaan Darah : Studi Kasus Unit Transfusi Darah Cabang - PMI Kota Malang. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN: 2548-964X, Vol. 2, No. 7, Juli 2018, pg. 2770-2779.
Sari, Ade Puspita. Optimasi Interval Fuzzy Time Series menggunakan Particle Swarm Optimization Untuk Memprediksi Kualitas Udara Di Kota Pekanbaru. Skripsi thesis 2019, Universitas Islam Negeri Sultan Syarif Kasim Riau.
Bahrudin, Muhammad Irfan . Optimisasi Daya Photovoltaic pada kondisi Partially Shaded dengan Maximum Power Point Tracker (MPPT) Menggunakan Metode Particle Swarm Optimization (PSO) Double Diode Model. Institut Teknologi Sepuluh Nopember. 2018
Tyas, F. A., Setianama, M., Fadilatul Fajriyah, R., & Ilham, A. (2021). Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(4). https://doi.org/10.22219/kinetik.v6i4.1330
Noviandi, N., & Ilham, A. (2020). Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1). https://doi.org/10.22219/kinetik.v5i1.985
Allen J. Wood, Power Generation Operation and Control, second edisi tahun 1996
Frisk, M. (2017). Simulation and optimization of a hybrid renewable energy system for appli- cation on a cuban farm
Saiprasad, N., Kalam, A., and Zayegh, A. (2018). Comparative study of optimization of hres using homer and ihoga software
Akhter MN, Mekhilef S, Mokhlis H, Shah NM.(2019). Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew Power Gener. 2019;13(7):1009–23. https://doi.org/10.1049/iet-rpg.2018.5649
Ahmed R, Sreeram V, Mishra Y, Arif D. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew Sustain Energy Rev. 2020;124:109792. https://doi.org/10.1016/j.rser.2020.109792
Sharadga H, Hajimirza S, Balog RS.(2020). Time series forecasting of solar power generation for large-scale photovoltaic plants. Renew Energy. 2020;150:797–807. https://doi.org/10.1016/j.renene.2019.12.131
Konstantinou M, Peratikou S, Charalambides AG.(2021). Solar photovoltaic forecasting of power outputusing LSTM net-works. Atmosphere. 2021;12(1):124. https://doi.org/10.3390/atmos12010124
Miraftabzadeh SM, Longo M, Foiadelli F.(2020. A-day-ahead photovoltaic power prediction based on long short term memory algorithm. In: SEST 2020—3rd international conference on smart energy systems and technologies. 2020.p. 1–6. https://doi.org/10.1109/SEST48500.2020.9203481