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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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Figures

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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. 2

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

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Fig. 3

Diagram of the PEMS for the positive energy building

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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. 9

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

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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. 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. 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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