Abstract
Due to the large number of grid connection of distributed power supply, the existing scheduling methods can not meet the demand gradually. The proposed virtual power plant provides a new idea to solve this problem. The photovoltaic power prediction provides the data basis for the scheduling of the virtual power plant. Prediction intervals of photovoltaic power is a powerful statistical tool used for quantifying the uncertainty of photovoltaic power generation in power systems. To improve the interval prediction accuracy during the non-stationary periods of photovoltaic power, this paper proposes a probabilistic ensemble prediction model, which combines the modules of data preprocessing, non-stationary period discrimination, feature extraction, deterministic prediction, uncertainty prediction, and optimization integration into a general framework. More specifically, in the non-stationary period discrimination module, the method of discriminating the difference of the power ratio difference is introduced and applied for identifying the non-stationary period of the data of photovoltaic output; in the deterministic point prediction module, a stacking- long-short-term memory neural network model is used for point forecasts; in the uncertainty interval prediction module, a BAYES neural network is introduced for probabilistic forecasts; in the optimization integration module, an optimization algorithm named Non-dominated Sorting Genetic Algorithm-II is applied for integrating and optimizing the results of the point forecast and probabilistic forecast. The proposed model is tested using two photovoltaic outputs and weather data measured from a grid-connected photovoltaic system. The results show that the proposed model outperforms conventional forecast methods to predict short-term photovoltaic power outputs and associated uncertainties. The interval width is reduced by 10–20%, and the prediction accuracy is improved by at least 10%; this can be a useful tool for photovoltaic power forecasting.
Highlights
In recent years, the fossil energy crisis has gradually attracted the attention of various countries, and the development momentum of new energy has been risen rapidly [1,2]
For power systems, when large-scale photovoltaic power generation systems are integrated into the main grid, they will have a great impact on the economic operation and stability of the grid, increasing the difficulty of power system dispatch [5,6]
To further improve forecasting performance, this paper proposes a multi-objective optimizationbased ensemble probability forecasting (MLBN) model for the non-stationary period of photovoltaic output based on the research of previous authors [25,26]
Summary
The fossil energy crisis has gradually attracted the attention of various countries, and the development momentum of new energy has been risen rapidly [1,2]. Modeling is divided into three stages: in the first stage, the historical output data of photovoltaic power plants are preprocessed, feature selection is performed based on the MIC theory, and the most suitable input features are selected; in the second stage, according to the feature selection results of the first stage, the features are respectively input to the improved LSTM algorithm and the BAYES neural network to obtain the initial deterministic point prediction results and the uncertainty interval prediction results; in the third stage, the initial interval prediction value is optimized to meet the narrowest interval width and the highest interval coverage, and the initial point prediction result is optimized and estimated, and it is expanded to a new interval prediction value.
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