Research Papers

Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System

[+] Author and Article Information
Martin Schmelas

Institute of Energy System Techniques,
Offenburg University of Applied Sciences,
Badstr. 24,
Offenburg 77652, Germany
e-mail: martin.schmelas@hs-offenburg.de

Thomas Feldmann, Jesus da Costa Fernandes, Elmar Bollin

Institute of Energy System Techniques,
Offenburg University of Applied Sciences,
Badstr. 24,
Offenburg 77652, Germany

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received April 15, 2014; final manuscript received November 24, 2014; published online January 27, 2015. Assoc. Editor: Santiago Silvestre.

J. Sol. Energy Eng 137(3), 031015 (Jun 01, 2015) (10 pages) Paper No: SOL-14-1119; doi: 10.1115/1.4029378 History: Received April 15, 2014; Revised November 24, 2014; Online January 27, 2015

Solar energy converted and fed to the utility grid by photovoltaic modules has increased significantly over the last few years. This trend is expected to continue. Photovoltaics (PV) energy forecasts are thus becoming more and more important. In this paper, the PV energy forecasts are used for a predictive energy management system (PEMS) in a positive energy building. The publication focuses on the development and comparison of different models for daily PV energy prediction taking into account complex shading, caused for example by trees. Three different forecast methods are compared. These are a physical model with local shading measurements, a multilayer perceptron neural network (MLP), and a combination of the physical model and the neural network. The results show that the combination of the physical model and the neural network provides the most accurate forecast values and can improve adaptability. From April to December, the mean percentage error (MPE) of the MLP with physical information is 11.6%. From December to March, the accuracy of the PV predictions decreases to an MPE of 78.8%. This is caused by poorer irradiation forecasts, but mainly by snow coverage of the PV modules.

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Pérez-Lombard, L., Ortiz, J., and Pout, C., 2008, “A Review on Buildings Energy Consumption Information,” Energy Build., 40(3), pp. 394–398. [CrossRef]
European Parliament-Committee on Industry, Research and Energy, “All New Buildings to Be Zero Energy From 2019,” Oct. 7, 2014. Available at: http://www.europarl.europa.eu/sides/getDoc.do?language=en&type=IM-PRESS&reference=20090330IPR52892
Paoli, C., Voyant, C., Muselli, M., and Nivet, M., 2010, “Forecasting of Preprocessed Daily Solar Radiation Time Series Using Neural Networks,” Solar Energy, 84(12), pp. 2146–2160. [CrossRef]
Mellit, A., and Pavan, A. M., 2010, “A 24-h Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant at Trieste, Italy,” Solar Energy, 84(5), pp. 807–821. [CrossRef]
Almonacid, F., Rus, C., Pérez-Higueras, P., and Hontoria, L., 2011, “Calculation of the Energy Provided by a PV Generator. Comparative Study: Conventional Methods vs. Artificial Neural Networks,” Energy, 36(1), pp. 375–384. [CrossRef]
Osterwald, C., 1986, “Translation of Device Performance Measurements to Reference Conditions,” Solar Cells, 18(3–4), pp. 269–279. [CrossRef]
Zhou, W., Yang, H., and Fang, Z., 2007, “A Novel Model for Photovoltaic Array Performance Prediction,” Appl. Energy, 84(12), pp. 1187–1198. [CrossRef]
De Soto, W., Klein, S. A., and Beckman, W. A., 2006, “Improvement and Validation of a Model for Photovoltaic Array Performance,” Solar Energy, 80(1), pp. 78–88. [CrossRef]
Taherbaneh, M., Farahani, G., and Rahmani, K., 2011, “Evaluation the Accuracy of One-Diode and Two-Diode Models for a Solar Panel Based Open-Air Climate Measurements,” Solar Cells-Silicon Wafer-Based Technologies, InTech, Rijeka, Croatia.
Ishaque, K., Salam, Z., and Taheri, H., 2011, “Simple, Fast and Accurate Two-Diode Model for Photovoltaic Modules,” Solar Energy Mater. Solar Cells, 95(2), pp. 586–594. [CrossRef]
Duffie, J. A., and Beckman, W. A., 1991, Solar Engineering of Thermal Processes, 2nd ed., Wiley, New York.
Su, Y., Chan, L., Shu, L., and Tsui, K., 2012, “Real-Time Prediction Models for Output Power and Efficiency of Grid-Connected Solar Photovoltaic Systems,” Appl. Energy, 93, pp. 319–326. [CrossRef]
Mellit, A., and Kalogirou, S. A., 2008, “Artificial Intelligence Techniques for Photovoltaic Applications: A Review,” Prog. Energy Combust. Sci., 34(5), pp. 574–632. [CrossRef]
Almonacid, F., Rus, C., Pérez, P. J., and Hontoria, L., 2009, “Estimation of the Energy of a PV Generator Using Artificial Neural Network,” Renewable Energy, 34(12), pp. 2743–2750. [CrossRef]
Chow, S. K. H., Lee, E. W. M., and Li, D. H. W., 2012, “Short-Term Prediction of Photovoltaic Energy Generation by Intelligent Approach,” Energy Build., 55, pp. 660–667. [CrossRef]
Sfetsos, A., and Coonick, A. H., 2000, “Univariate and Multivariate Forecasting of Hourly Solar Radiation With Artificial Intelligence Techniques,” Solar Energy, 68(2), pp. 169–178. [CrossRef]
Yona, A., Senjyu, T., Saber, A., Funabashi, T., Sekine, H., and Chul, H. K., 2008, “Application of Neural Network to 24-hour-Ahead Generating Power Forecasting for PV System,” 2008 IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–6. [CrossRef]
Quaschning, V., 1996, Simulation der Abschattungsverluste bei solarelektrischen Systemen, 1st ed., Köster, Berlin.
Drif, M., Pérez, P. J., Aguilera, J., and Aguilar, J. D., 2008, “A New Estimation Method of Irradiance on a Partially Shaded PV Generator in Grid-Connected Photovoltaic Systems,” Renewable Energy, 33(9), pp. 2048–2056. [CrossRef]
Syafaruddin, Karatepe, E., and Hiyama, T., 2009, “Artificial Neural Network-Polar Coordinated Fuzzy Controller Based Maximum Power Point Tracking Control Under Partially Shaded Conditions,” Renewable Power Gener., IET, 3(2), pp. 239–253. [CrossRef]
Deline, C. A., 2009, “Partially Shaded Operation of a Grid-Tied PV System,” Proceedings of the 34th IEEE Photovoltaic Specialists Conference (PVSC), Philadelphia, PA, June 7–12, pp. 001268–001273. [CrossRef]
Schwarz, “Impressionen des Effizienzhaus Plus,” Accessed Oct. 7, 2014. Available at: http://www.bmvbs.de/SharedDocs/DE/Fotoreihen/Mediathek/Presse-und-Leitungstermine/2011/111206-ehp-impressionen.html?nn=75502
Figueiredo, J., and Sá da Costa, J., 2012, “A SCADA System for Energy Management in Intelligent Buildings,” Energy Build., 49, pp. 85–98. [CrossRef]
Lefort, A., Bourdais, R., Ansanay-Alex, G., and Guéguen, H., 2013, “Hierarchical Control Method Applied to Energy Management of a Residential House,” Energy Build., 64, pp. 53–61. [CrossRef]
Perez, E., Beltran, H., Aparicio, N., and Rodriguez, P., 2013, “Predictive Power Control for PV Plants With Energy Storage,” IEEE Trans. Sustain. Energy, 4(2), pp. 482–490. [CrossRef]
Zhang, H., Davigny, A., Colas, F., Poste, Y., and Robyns, B., 2012, “Fuzzy Logic Based Energy Management Strategy for Commercial Buildings Integrating Photovoltaic and Storage Systems,” Energy Build., 54, pp. 196–206. [CrossRef]
Walraven, R., 1978, “Calculating the Position of the Sun,” Solar Energy, 20(5), pp. 393–397. [CrossRef]
Reindl, D. T., Beckman, W. A., and Duffie, J. A., 1990, “Diffuse Fraction Correlations,” Solar Energy, 45(1), pp. 1–7. [CrossRef]
Temps, R. C., and Coulson, K. L., 1977, “Solar Radiation Incident Upon Slopes of Different Orientations,” Solar Energy, 19(2), pp. 179–184. [CrossRef]
Luque, A., and Hegedus, S., 2003, Handbook of Photovoltaic Science and Engineering, Wiley, Hoboken, NJ [CrossRef].
Haeberlin, H., Borgia, L., Kaempfer, M., and Zwahle, U., 2006, New Tests at Grid-Connected PV Inverters: Overview Over Test Results and Measured Values of Total Efficiency ηtot, 21st European Photovoltaic Solar Energy Conference, Dresden.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing: Explorations in the Microstructure of Cognition,” Vol. 1, D. E.Rumelhart, J. L.McClelland, and PDP Research Group C, eds., MIT Press, Cambridge, MA, pp. 318–362.
Kalogirou, S. A., 2001, “Artificial Neural Networks in Renewable Energy Systems Applications: A Review,” Renewable Sustainable Energy Rev., 5(4), pp. 373–401. [CrossRef]
Almonacid, F., Rus, C., Hontoria, L., and Muñoz, F. J., 2010, “Characterisation of PV CIS Module by Artificial Neural Networks. A Comparative Study With Other Methods,” Renewable Energy, 35(5), pp. 973–980. [CrossRef]
Mcgill, R., Tukey, J. W., and Larsen, W. A., 1978, “Variations of Box Plots,” Am. Statist., 32(1), pp. 12–16. [CrossRef]


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Fig. 3

Diagram of the PEMS for the positive energy building

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Fig. 2

Shading situation of the positive energy building, which is located in the middle of the figure

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Fig. 1

South view of the positive energy building with a view to the thin film PV generator on the southwest façade

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Fig. 4

Conceptual diagram of a physical model, an ANN without physical information model and an ANN with physical information model with weather predictions for PV energy predictions

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Fig. 5

Shot of a horizontoscope which shows the times of shading and direct sunlight exposure

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Fig. 6

Comparison between predicted and measured hourly PV power for the physical model, the ANN without physical information, the ANN with physical information, and the irradiance forecast

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Fig. 7

Box plot for the visualization of the distribution of the MBE of daily PV energy for the different PV forecast models and the irradiance forecast subdivided into months with a different scale for snowy months

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Fig. 8

Three typical different sample days (sunny, partly cloudy, and very cloudy) to compare the measured and predicted PV power of the three forecast models as well as the measured and predicted irradiance forecast of the DWD

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Fig. 9

Typical sample day with snow on the PV modules to compare measured and predicted PV power and irradiance



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