Abstract
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.
Highlights
In recent years, an increasing number of photovoltaic (PV) power generations have been connected to the distribution network
Sci. 2019, 9, 1487 prediction value based on the phase space reconstruction and deep neural network (DNN), and the9 blue of 11 dotted line is the ultrashort-term prediction value based on the traditional back propagation (BP) neural network
Compared with the prediction model based on the traditional BP neural network, the forecasting scheme proposed in this paper improved the accuracy of net load forecasting under different weather conditions
Summary
An increasing number of photovoltaic (PV) power generations have been connected to the distribution network. Liang et al [18] propose a hybrid model that combines the empirical mode decomposition (EMD), minimal redundancy maximal relevance (mRMR), general regression neural network (GRNN), and fruit fly optimization algorithm (FOA). For the real-time safety analysis of power systems and the reliable operation of economic dispatch, a more detailed ultrashort-term prediction is required. Considering the volatility of the distributed PV power generation and the real-time requirements of the ultrashort-term prediction, the PV power should be considered a load and merged with the traditional load to form a net load [20,21,22]. Compared with the single hidden layer neural network, DNN can fit the historical data better and significantly improve the accuracy of ultrashort-term load forecasting
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
