DOI:10.1007/s40565-018-0393-5 |
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Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks |
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Net amount: 865 |
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Author:
Saad Parvaiz DURRANI1, Stefan BALLUFF1, Lukas WURZER2, Stefan KRAUTER1
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Author Affiliation:
1. Paderborn University, Warburger Str. 98, 33098 Paderborn, Germany; 2. Bosch Thermotechnik GmbH, Junkerstrasse 20, 73249 Wernau, Germany
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Foundation: |
The framework for this research was given by the Energy Monitoring and Management Project by Bosch Thermotechnik GmbH, Germany. |
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Abstract: |
In order to develop predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems, accurate and reliable photovoltaic (PV) power forecasts are required. A PV yield prediction system is presented based on an irradiance forecast model and a PV model. The PV power forecast is obtained from the irradiance forecast using the PV model. The proposed irradiance forecast model is based on multiple feed-forward neural networks. The global horizontal irradiance forecast has a mean absolute percentage error of 3.4% on a sunny day and 23% on a cloudy day for Stuttgart. PV power forecasts based on the neural network irradiance forecast have performed much better than the PV power persistence forecast model. |
Keywords: |
Grid connected photovoltaic (GCPV), Photovoltaic (PV), PV power prediction, Irradiance forecast, Neural network (NN) |
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Online Time:2018/03/20 |
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