Abstract
Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.
Highlights
As energy and environmental issues become increasingly prominent, the effective and efficient utilization of renewable energy sources is a global challenge [1]
The input and output of a set of samples are converted into a nonlinear optimization problem, and the most common gradient descent method in optimization is adopted in a traditional back propagation (BP) neural network, which is effective to simulate and emulate nonlinear objects
Scientific selection of input and output parameters and the sufficiency of training samples are of great significance to a BP neural network
Summary
As energy and environmental issues become increasingly prominent, the effective and efficient utilization of renewable energy sources is a global challenge [1]. Considering the dynamic characteristics of the PV power generation process, the accuracy of an offline trained neural network forecasting method is not ideal [15]. Dolara et al [23] proposed the hybrid method, combining an artificial intelligence technique with an analytical physical model, and the results showed that the length training set is critical to the dynamic characteristics of neural networks. In order to counter the variable and intermittent challenges in the power output due to time-varying factors, Ceci et al [25] and Saberian et al [26] studied the problem of PV energy prediction by considering the learning setting and the learning algorithm, and found that the structure of the neural network is critical to the accuracy of the power prediction.
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