Research on the Prediction Model of Urban Power Grid Short-Term Load Based on BP Neural Network Algorithm

Pei Long Xu

doi:10.4028/www.scientific.net/AMR.926-930.954

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Research on the Prediction Model of Urban Power...

Research on the Prediction Model of Urban Power Grid Short-Term Load Based on BP Neural Network Algorithm

Abstract:

Objective: The paper aims to establish the prediction model of urban power grid short-term load based on BP neural network algorithm. Method: Five factors influencing the urban power grid short-term load are used to establish the neural network model: date type, weather, daily maximum temperature, daily minimum temperature and daily average temperature. Matlab toolbox is used to develop the testing platform through VC++ programming. Result: The variable learning rates are 0.35 and 0.64. With 23410 iterations, the model is converged, and the global error is 0.00032. Conclusion: Through the data comparison and analysis, the relative error is within 5%, thus indicating the model is accurate and effective, and it can be used to predict the change of urban power grid short-term load.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

954-957

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.954

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Online since:

May 2014

Authors:

Pei Long Xu*

Keywords:

BP Model, Power Grid Short-Term Load, Predication

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* - Corresponding Author

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