Abstract
In recent years, insufficient access to energy and environmental challenges caused the push for clean and sustainable means of power generation. The integration of renewable energy resources into the electricity grid is known to reduce Greenhouse Gas (GHG) emissions and environmental pollution. Solar power has the enormous benefit of availability and can often be accessed cost effectively. However, there is a recurring problem of intermittency and variability in most solar power generation systems. The variability and intermittent nature of solar power sources introduce significant challenges in the planning and scheduling of smart grids. Solar power prediction can mitigate this variability and improve the integration of solar power resources into smart grids. This paper presents an Artificial Neural Network (ANN) model for solar power prediction, and assesses how several weather input variables from Leeds, UK, affect the prediction accuracy. Following this, the Nonlinear Autoregressive Exogenous Neural Network (NARX NN) model performance is compared with Nonlinear Autoregressive Neural Network (NAR NN) model using a time series modelling approach. The result shows that NARX NN model outperformed NAR NN model for the studied geographical location.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Araújo, R. A., Oliveira, A. L. I., & Meira, S. (2017). A morphological neural network for binary classification problems. Engineering Applications of Artificial Intelligence, 65, 12–28.
Chen, X., Du, Y., Xiao, W., & Lu, S., 2017. Power ramp-rate control based on power forecasting for PV grid-tied systems with minimum energy storage. IEEE Conference 2017.
Chu, Y., Urquhart, B., Gohari, S. M., & Pedro, H. T. C. (2015). Short-term reforecasting of power output from a 48 MWe solar PV plant. Solar Energy, 112, 68–77.
GlobalData. (2017). Global PV capacity is expected to reach 969GW by 2025. Power Technology.
Gong, T., Fan, T., Guo, J., & Cai, Z. (2017). GPU-based parallel optimization of immune convolutional neural network and embedded system. Engineering Applications of Artificial Intelligence, 62, 384–395.
Gurney, K. (1997). An introduction to neural networks. ROUTLEDGE, UCL Press Limited 11 New Fetter Lane London EC4P 4EE, UCL Press Limited is an imprint of the Taylor & Francis Group, Record Number: 163. https://www.inf.ed.ac.uk/teaching/courses/nlu/assets/reading/Gurney_et_al.pdf.
Haykin, S. (2005). Neural Network: A Comprehensive Foundation, Pearson Education (Singapore) Pte. Ltd., Indian Branch, 482 F. I. E. Patparganj Delhi 110092, India, Pearson Prentice Hall.
IEA. (2015). Energy from the desert: Very large scale PV power plants for shifting to renewable energy future. International Energy Agency Photovoltaic Power Systems Program.
IEA. (2018). Electricity generation from renewables by source World 1990–2016. International Energy Agency. Key World Energy Statistics 2018
IRENA. (2017). Renewable energy: A key climate solution. International Renewable Energy Agency.
IRENA. (2019). Innovation landscape for a renewable-powered future: Solutions to integrate variable renewables. International Renewable Energy Agency, Abu Dhabi.
Kalogirou. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1–2), 17–35.
Mitchell, T. M. (1997). Machine learning, McGraw-Hill Science/Engineering/Math. Retrieved from https://www.cs.ubbcluj.ro/~gabis/ml/ml-books/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf.
Mohammed, L. B., Hamdan, M. A., & Abdelhafez, E. A. (2013). Hourly solar radiation prediction based on Nonlinear Autoregressive Exogenous (Narx) neural network. Jordan Journal of Mechanical and Industrial Engineering, 7, 11–18.
Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Retrieved from http://www.cesarzamudio.com/uploads/1/7/9/1/17916581/nagelkerke_n.j.d._1991_-_a_note_on_a_general_definition_of_the_coefficient_of_determination.pdf. Biometrika trust http://links.jstor.org/sici?sici=0006-3444%28199109%2978%3A3%3C691%3AANOAGD%3E2.0.CO%3B2-V.
Nuchhi, S. S., Sali, R. B., & Ankaliki, S. G. (2013). Effect of reactive power compensation on voltage profile. International Journal of Engineering Research and Technology, 2(6), 2627.
Pelland, S., Galanis, G., & Kallos, G. (2011). Solar and photovoltaic forecasting through post- processing of the Global Environmental Multiscale numerical weather prediction model. Progress in Photovoltaics: Research and Applications, 21(3). https://doi.org/10.1002/pip.1180
Raza, M. Q., Mithulananthan, N., & Summerfield, A. (2018). Solar output power forecast using an ensemble framework with neural predictors and Bayesian adaptive combination. Solar Energy, 166, 226–241.
Siegelman & Sontag. (1992). On the computational power of neural nets. In Proceedings of the fifth annual workshop on Computational learning theory. ACM conference Proceedings, 440–449.
Tanti, T. (2018). The key trends that will shape renewable energy in 2018 and beyond. World Economic Forum.
Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49, 1225–1231.
Ueckerdt, F., Brecha, R., & Luderer, G. (2015). Analyzing major challenges of wind and solar variability in power systems. Renewable Energy, 81, 1–10.
Vernier. (2001). What are mean squared error and root mean squared error? Beaverton, OR: Vernier Software & Technology. Retrieved from https://www.vernier.com/til/1014/.
Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2015). Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems, 1, 38–46.
WEO. (2019). World Energy Outlook 2017: A world in transformation. IEA.
Wu, C., Wen, F., Lou, Y., & Xin, F. (2015). Probabilistic load flow analysis of photovoltaic generation system with plug-in electric vehicles. International Journal of Electrical Power & Energy Systems, 64, 1221–1228.
Zou, C., Zhao, Q., Zhang, G., & Xiong, B. (2016). Energy revolution: From a fossil energy era to a new energy era. Natural Gas Industry B, 3, 1–11.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Aningo, N.U., Hardy, A., Glew, D. (2020). Evaluating Solar Prediction Methods to Improve PV Micro-grid Effectiveness Using Nonlinear Autoregressive Exogenous Neural Network (NARX NN). In: Scott, L., Dastbaz, M., Gorse, C. (eds) Sustainable Ecological Engineering Design. Springer, Cham. https://doi.org/10.1007/978-3-030-44381-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-44381-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44380-1
Online ISBN: 978-3-030-44381-8
eBook Packages: EnergyEnergy (R0)