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

• 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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9071487

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.