Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network
Journal of Energy, Environmental & Chemical Engineering
Volume 2, Issue 2, June 2017, Pages: 32-40
Received: Jun. 1, 2017; Accepted: Jun. 15, 2017; Published: Jul. 26, 2017
Views 2132      Downloads 120
Samuel Bimenyimana, Department of Electrical Engineering, Hebei University of Technology, Tianjin, China
Godwin Norense Osarumwense Asemota, Department of Electrical Engineering, University of Rwanda, School of Engineering Kigali, Rwanda
Li Lingling, Department of Electrical Engineering, Hebei University of Technology, Tianjin, China
Article Tools
Follow on us
Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.
Photovoltaic Output Power, Prediction, Empirical Formula, Temperature, Nonlinear Autoregressive Neural Network
To cite this article
Samuel Bimenyimana, Godwin Norense Osarumwense Asemota, Li Lingling, Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network, Journal of Energy, Environmental & Chemical Engineering. Vol. 2, No. 2, 2017, pp. 32-40. doi: 10.11648/j.jeece.20170202.13
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Hitachi (2016) Photovoltaic Power Generation System online http://www.hitachi.com/products/power/solar-power/outline/. (Accessed 28 May 2017).
Bratley, J. (2016) Solar Panel Diagram http://www.clean-energy-ideas.com/solar/solar-panels/solar-panel-diagram. (Accessed 28 May 2017).
Electical4u (2017) Components of a Solar Electric Generation System http://www.electrical4u.com/components-of-a-solar-electric-generation-system/ (Accessed 28 May 2017).
Brano, V. L., Orioli, A., Ciulla, G., & Di Gangi, A. (2010). An improved five-parameter model for photovoltaic modules. Solar Energy Materials and Solar Cells, 94 (8), 1358-1370.
Shi, J., Lee, W. J., Liu, Y., Yang, Y., & Wang, P. (2012). Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Transactions on Industry Applications, 48 (3), 1064-1069.
Pelland, S., Remund, J., Kleissl, J., Oozeki, T., & De Brabandere, K. (2013). Photovoltaic and solar forecasting: state of the art. IEA PVPS, Task, 14, 1-36.
Yang, H. T., Huang, C. M., Huang, Y. C., & Pai, Y. S. (2014). A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE transactions on sustainable energy, 5 (3), 917-926.
Paulescu, M., Paulescu, E., Gravila, P., & Badescu, V. (2012). Weather modeling and forecasting of PV systems operation. Springer Science & Business Media.
Huang, Y., Lu, J., Liu, C., Xu, X., Wang, W., & Zhou, X. (2010, October). Comparative study of power forecasting methods for PV stations. In Power System Technology (POWERCON), 2010 International Conference on (pp. 1-6). IEEE.
Ogliari, E., Grimaccia, F., Leva, S., & Mussetta, M. (2013). Hybrid predictive models for accurate forecasting in PV systems. Energies, 6 (4), 1918-1929.
Kou, J., Liu, J., Li, Q., Fang, W., Chen, Z., Liu, L., & Guan, T. (2013, October). Photovoltaic power forecasting based on artificial neural network and meteorological data. In TENCON 2013-2013 IEEE Region 10 Conference (31194) (pp. 1-4). IEEE.
Yona, A., Senjyu, T., Saber, A. Y., Funabashi, T., Sekine, H., & Kim, C. H. (2007, November). Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. In Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference on (pp. 1-6). IEEE.
Cococcioni, M., D'Andrea, E., & Lazzerini, B. (2011, November). 24-hour-ahead forecasting of energy production in solar PV systems. In Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on (pp. 1276-1281). IEEE.
Kardakos, E. G., Alexiadis, M. C., Vagropoulos, S. I., Simoglou, C. K., Biskas, P. N., & Bakirtzis, A. G. (2013, September). Application of time series and artificial neural network models in short-term forecasting of PV power generation. In Power Engineering Conference (UPEC), 2013 48th International Universities' (pp. 1-6). IEEE.
Chupong, C., & Plangklang, B. (2011). Forecasting power output of PV grid connected system in Thailand without using solar radiation measurement. Energy Procedia, 9, 230-237.
Chen, C., Duan, S., Cai, T., & Liu, B. (2011). Online 24-h solar power forecasting based on weather type classification using artificial neural network. Solar Energy, 85 (11), 2856-2870.
Ashraf, I., & Chandra, A. (2004). Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant. International journal of global energy issues, 21 (1-2), 119-130.
Wu, Y. K., Chen, C. R., & Abdul Rahman, H. (2014). A novel hybrid model for short-term forecasting in PV power generation. International Journal of Photoenergy, 2014.
Ciabattoni, L., Grisostomi, M., Ippoliti, G., Longhi, S., & Mainardi, E. (2012, June). Online tuned neural networks for PV plant production forecasting. In Photovoltaic Specialists Conference (PVSC), 2012 38th IEEE (pp. 002916-002921). IEEE.
Al-Messabi, N., Li, Y., El-Amin, I., & Goh, C. (2012, June). Forecasting of photovoltaic power yield using dynamic neural networks. In Neural Networks (IJCNN), The 2012 International Joint Conference on (pp. 1-5). IEEE.
Kleissl, J. (2013). Solar energy forecasting and resource assessment. Academic Press.
Saberian, A., Hizam, H., Radzi, M. A. M., Ab Kadir, M. Z. A., & Mirzaei, M. (2014). Modelling and prediction of photovoltaic power output using artificial neural networks. International Journal of Photoenergy, 2014.
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2 (5), 359-366.
Martín, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., & Cony, M. (2010). Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 84 (10), 1772-1781.
Demuth, H. B., Beale, M. H., De Jess, O., & Hagan, M. T. (2014). Neural network design. Martin Hagan.
Specht, D. F. (1991). A general regression neural network. IEEE transactions on neural networks, 2 (6), 568-576.
Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural Networks, 2 (2004), 41.
Dunlop, E. D., Wald, L., & Suri, M. (2006). Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale (p. 205). Nova Science Publishers Inc.
Tudelft Solar Cell Paramters and Equivalent Circuit https://ocw.tudelft.nl/wp-content/uploads/solar_energy_section_9_1_9_3.pdf (Accessed 28 May 2017).
Tsai, C. T., Kuo, Y. C., Kuo, Y. P., & Hsieh, C. T. (2015). A Reflex Charger with ZVS and Non-Dissipative Cells for Photovoltaic Energy Conversion. Energies, 8 (2), 1373-1389.
Anku, N.E., Adu-Gyamfi, D., Kankam, A., Takyi, A. and Amponsah, R., 2015. A Model for Photovoltaic Module Optimisation. Journal of Mechanical Engineering and Automation, 5 (2), pp. 72-79.
Electrical4u (2017) What is a Solar PV module https://www.electrical4u.com/what-is-a-solar-pv-module/ (Accessed 28 May 2017).
Kandil M, K., Majida S, A., Asma M, A. A., Latifa M, A., Ibrahim M, K., & Adel A, G. (2011). Investigation of the performance of CIS photovoltaic modules under different environmental conditions. Smart Grid and Renewable Energy, 2011.
My Electrical (2017) Photovoltaic (PV) Electrical Calculations http://myelectrical.com/notes/entryid/225/photovoltaic-pv-electrical-calculations (Accessed 28 May 2017).
EMSD Solar Photovoltaic http://re.emsd.gov.hk/english/solar/solar_ph/solar_ph_to.html (Accessed 28 May 2017).
Aparicio, M. P., Sebastiá, J. P., Devesa, T. C. S., & Sanjuan, J. V. L. (2013). Modeling of photovoltaic cell using free software application for training and design circuit in photovoltaic solar energy (pp. 121-139). InTech.
Jiang, H., & Hong, L. (2013). Application of BP Neural Network to Short-Term-Ahead Generating Power Forecasting for PV System. In Advanced Materials Research (Vol. 608, pp. 128-131). Trans Tech Publications.
Tiwari, G. N., & Dubey, S. (2010). Fundamentals of photovoltaic modules and their applications (No. 2). Royal Society of Chemistry.
Ding, M., Wang, L., & Bi, R. (2011). An ANN-based approach for forecasting the power output of photovoltaic system. Procedia Environmental Sciences, 11, 1308-1315.
PVPOWER Measuring PV Efficiency http://www.pvpower.com/assets/Measuring-PV-Efficiency-Solar-Panels.pdf (Accessed 28 May 2017).
Solanki, C. S. (2013). Solar photovoltaic technology and systems: A manual for technicians, trainers and engineers. PHI Learning Pvt. Ltd.
Chikate, B. V., & Sadawarte, Y. A. The Factors Affecting the Performance of Solar Cell. International Journal of Computer Applications (0975-8887).
Aish, M. Q. (2015). Temperature effect on Photovoltaic Modules Power drop. Al-khwarizmi Engineering Journal, 11 (2), 62, 73.
Rwanda Meteorology Agency http://www.meteorwanda.gov.rw/index.php?id=2 (Accessed 28 May 2017).
Peterson, C., & Rögnvaldsson, T. S. (1991). An introduction to artificial neural networks (No. LU-TP-91-23). CERN.
PSYCH. Artificial Neural Networks Technology http://www.psych.utoronto.ca/users/reingold/courses/ai/cache/neural2.html (Accessed 28 May 2017).
Wikipedia, Artificial Neural Network https://en.wikipedia.org/wiki/Artificial_neural_network (Accessed 28 May 2017).
Zhu, H., Li, X., Sun, Q., Nie, L., Yao, J., & Zhao, G. (2015). A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks. Energies, 9 (1), 11.
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016). On recent advances in PV output power forecast. Solar Energy, 136, 125-144.
Ruiz, L. G. B., Cuéllar, M. P., Calvo-Flores, M. D., & Jiménez, M. D. C. P. (2016). An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings. Energies, 9 (9), 684.
Mathworks 20017 Neural Network Time Series Prediction and Modelling http://www.mathworks.com/help/nnet/gs/neural-network-time-series-prediction-and-modeling.html (Accessed 28 May 2017).
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186