An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term fo...

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Bibliographic Details
Published in:Applied sciences 2019-04, Vol.9 (7), p.1487
Main Authors: Mei, Fei, Wu, Qingliang, Shi, Tian, Lu, Jixiang, Pan, Yi, Zheng, Jianyong
Format: Article
Language:English
Subjects:
Artificial intelligence
Debugging
Decomposition
deep neural network
Distributed generation
Electric power distribution
Electric power generation
Electrical engineering
Electricity consumption
Electricity distribution
Energy sources
Forecasting
Machine learning
net load forecasting
Neural networks
Optimization algorithms
phase space reconstruction
Power plants
Power supplies
Power supply
Reconstruction
Short term
Time series
Volatility
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Summary:Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9071487