DocumentCode
2180946
Title
The application of photovoltaic power prediction technology
Author
Wang, Zengxin ; Su, Shi ; Zhang, Shaoquan
Author_Institution
Grid Co. Postgrad. Workstation, North China Electr. Power Univ. & Yunnan Power, Kunming, China
fYear
2011
fDate
9-11 Sept. 2011
Firstpage
2343
Lastpage
2346
Abstract
Power load forecasting is an important part of the power system planning, and accurate load forecasting can provide necessary basis data for the dispatcher, which is extremely important in the planning and operation of power system. Neural network can approximate any nonlinear mapping with arbitrary precision, and its distributed information storage and processing structure have a certain fault tolerant. So neural network is suitable for complex system modeling, and it can be used as the main method to predict photovoltaic power operation state variables. This paper uses the neural network algorithm to establish photovoltaic power system´s load forecast model, and except the generate historical data, meteorological forecast information is added to the algorithm, then the model is trained and tested, the high precision prediction results can show the effectiveness of the algorithm.
Keywords
fault tolerance; load forecasting; neural nets; photovoltaic power systems; power engineering computing; power generation planning; distributed information storage; fault tolerant; neural network; photovoltaic power operation; photovoltaic power prediction technology; power load forecasting; power system planning; Biological neural networks; Neurons; Photovoltaic systems; Power systems; Predictive models; distributed information; load forecasting; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronics, Communications and Control (ICECC), 2011 International Conference on
Conference_Location
Zhejiang
Print_ISBN
978-1-4577-0320-1
Type
conf
DOI
10.1109/ICECC.2011.6066746
Filename
6066746
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