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Short-term Power Prediction of the Photovoltaic System Based on QPSO-SVM
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lei, Yang Zhou, Shiping Xia, Yongjun Shu, Xin |
| Copyright Year | 2014 |
| Abstract | Short-term power prediction of the photovoltaic system is one of the effective means to reduce the adverse effects of photovoltaic power on the grid. Since the efficiency of the traditional support vector machine (SVM) prediction method is low, this paper proposes the SVM based on the parameter optimization method of quantum particle swarm optimization (QPSO), and then apply into the power shortterm prediction of the photovoltaic system. After comparing and analyzing the prediction results of SVM based on three optimization methods, we find that the QPSO-SVM method has better precision and stability, which provides reference to forecast generation power of the photovoltaic system. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iaescore.com/journals/index.php/IJEECS/article/download/3708/1995 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Mathematical optimization Particle swarm optimization Population Parameter Projections and Predictions Software release life cycle Support vector machine Test data edotreotide gallium ga-68 |
| Content Type | Text |
| Resource Type | Article |
