Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 767284, 9 pages
http://dx.doi.org/10.1155/2013/767284
Research Article

Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

1Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
2Department of Electrical Engineering, University of La Rioja, Luis de Ulloa 20, 26004 Logroño, Spain
3Department of Electrical Engineering, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain

Received 5 September 2013; Revised 19 October 2013; Accepted 19 October 2013

Academic Editor: Massimo Scalia

Copyright © 2013 Claudio Monteiro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [24 citations]

The following is the list of published articles that have cited the current article.

  • A. Dolara, S. Leva, M. Mussetta, and E. Ogliari, “PV hourly day-ahead power forecasting in a micro grid context,” 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), pp. 1–5, . View at Publisher · View at Google Scholar
  • Loredana Cristaldi, Giacomo Leone, and Roberto Ottoboni, “A hybrid approach for solar radiation and photovoltaic power short-term forecast,” 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6, . View at Publisher · View at Google Scholar
  • Jonas Queiroz, Artur Dias, and Paulo Leitao, “Predictive data analytics for agent-based management of electrical micro grids,” IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, pp. 004684–004689, . View at Publisher · View at Google Scholar
  • Emanuele Ogliari, Alessandro Gandelli, Francesco Grimaccia, Sonia Leva, and Marco Mussetta, “Neural forecasting of the day-ahead hourly power curve of a photovoltaic plant,” 2016 International Joint Conference on Neural Networks (IJCNN), pp. 654–659, . View at Publisher · View at Google Scholar
  • Khola Malik, Bilal Ahmed Bhatti, and Farrukh Kamran, “An approach to predict output of PV panels using weather corrected global irradiance,” 2016 International Conference on Intelligent Systems Engineering (ICISE), pp. 111–117, . View at Publisher · View at Google Scholar
  • Jonas Queiroz, Paulo Leitao, and Artur Dias, “Predictive data analysis driven multi-agent system approach for electrical micro grids management,” 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE), pp. 738–743, . View at Publisher · View at Google Scholar
  • Xingyu Yan, Dhaker Abbes, and Bruno Francois, “Solar radiation forecasting using artificial neural network for local power reserve,” 2014 International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2014, 2014. View at Publisher · View at Google Scholar
  • Jiao Shi, Jiaji Wu, Anand Paul, Licheng Jiao, and Maoguo Gong, “Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms,” Mathematical Problems in Engineering, vol. 2014, pp. 1–15, 2014. View at Publisher · View at Google Scholar
  • Alberto Dolara, Sonia Leva, and Giampaolo Manzolini, “Comparison of different physical models for PV power output prediction,” Solar Energy, vol. 119, pp. 83–99, 2015. View at Publisher · View at Google Scholar
  • S. Leva, A. Dolara, F. Grimaccia, M. Mussetta, and E. Ogliari, “Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power,” Mathematics and Computers in Simulation, 2015. View at Publisher · View at Google Scholar
  • Alberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta, and Emanuele Ogliari, “A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output,” Energies, vol. 8, no. 2, pp. 1138–1153, 2015. View at Publisher · View at Google Scholar
  • Nicolas Perez-Mora, Vincent Canals, Víctor Martínez-Moll, Nicolas Perez-Mora, Vincent Canals, and Victor Martinez-Moll, “Short-term Spanish aggregated solar energy Forecast,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9095, pp. 307–319, 2015. View at Publisher · View at Google Scholar
  • Nicolas Perez-Mora, Victor Martinez-Moll, and Vincent Canals, “Spanish renewable energy generation short-term forecast,” ISES Solar World Congress 2015, Conference Proceedings, pp. 216–227, 2015. View at Publisher · View at Google Scholar
  • Emanuele Ogliari, Alberto Bolzoni, Sonia Leva, and Marco Mussetta, “Day-ahead PV Power Forecast by Hybrid ANN Compared to the Five Parameters Model Estimated by Particle Filter Algorithm,” Artificial Neural Networks and Machine Learning – ICANN 2016, vol. 9887, pp. 291–298, 2016. View at Publisher · View at Google Scholar
  • J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F.J. Martinez-de-Pison, and F. Antonanzas-Torres, “Review of photovoltaic power forecasting,” Solar Energy, vol. 136, pp. 78–111, 2016. View at Publisher · View at Google Scholar
  • Antonello Rosato, Rosa Altilio, Rodolfo Araneo, and Massimo Panella, “Prediction in Photovoltaic Power by Neural Networks †,” Energies, vol. 10, no. 7, pp. 1003, 2017. View at Publisher · View at Google Scholar
  • Luca Migliorini, Luca Molinaroli, Riccardo Simonetti, and Giampaolo Manzolini, “Development and experimental validation of a comprehensive thermoelectric dynamic model of photovoltaic modules,” Solar Energy, vol. 144, pp. 489–501, 2017. View at Publisher · View at Google Scholar
  • Rodolfo Dufo-López, L. Alfredo Fernández-Jiménez, Ignacio J. Ramírez-Rosado, J. Sergio Artal-Sevil, José A. Domínguez-Navarro, and José L. Bernal-Agustín, “Daily operation optimisation of hybrid stand-alone system by model predictive control considering ageing model,” Energy Conversion and Management, vol. 134, pp. 167–177, 2017. View at Publisher · View at Google Scholar
  • Ruby Nageem, and Jayabarathi R, “Predicting the Power Output of a Grid-Connected Solar Panel Using Multi-Input Support Vector Regression,” Procedia Computer Science, vol. 115, pp. 723–730, 2017. View at Publisher · View at Google Scholar
  • Emanuele Ogliari, Alberto Dolara, Giampaolo Manzolini, and Sonia Leva, “Physical and hybrid methods comparison for the day ahead PV output power forecast,” Renewable Energy, vol. 113, pp. 11–21, 2017. View at Publisher · View at Google Scholar
  • Junseo Son, Yongtae Park, Junu Lee, and Hyogon Kim, “Sensorless PV Power Forecasting in Grid-Connected Buildings through Deep Learning,” Sensors, vol. 18, no. 8, pp. 2529, 2018. View at Publisher · View at Google Scholar
  • Emanuele Ogliari, Alessandro Niccolai, Sonia Leva, and Riccardo Zich, “Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed,” Energies, vol. 11, no. 6, pp. 1487, 2018. View at Publisher · View at Google Scholar
  • Alberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta, and Emanuele Ogliari, “Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning,” Applied Sciences, vol. 8, no. 2, pp. 228, 2018. View at Publisher · View at Google Scholar
  • Utpal Kumar Das, Kok Soon Tey, Mehdi Seyedmahmoudian, Saad Mekhilef, Moh Yamani Idna Idris, Willem Van Deventer, Bend Horan, and Alex Stojcevski, “Forecasting of photovoltaic power generation and model optimization: A review,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 912–928, 2018. View at Publisher · View at Google Scholar