Abstract
Under the background of the continuous development of photovoltaic power generation technology, accurate prediction of photovoltaic output power has become an important subject. In this paper, a combined method of two-model based on forecasting meteorological data for photovoltaic power generation forecasting is proposed. To solve the problem of the adaptability of a single model, two different models are used according to the different types of output power characteristics. The K-means clustering algorithm is used to classify different weather types according to the historical meteorological data. After predicting the irradiance and temperature of the period to be predicted and classifying the period into different types, the photovoltaic output power is predicted by a suitable model. The two prediction models are the Wavelet- Decomposition-ARIMA model and EDM-SA-DBN model, which are suitable for periods with larger and smaller fluctuation amplitude of photovoltaic output, respectively. Wavelet decomposition can refine the data with large fluctuations on multiple scales, make the data smooth, and improve the prediction accuracy of the Autoregressive Integrated Moving Average model (ARIMA). The Deep Belief Network (DBN) can effectively process a large number of complex data and deep mining the data features. While the empirical mode decomposition (EMD) can decompose the more stable data and amplify the details in the signal as much as possible. Meanwhile, the simulated annealing algorithm (SA) can avoid the network falling into a local optimal solution and improve the prediction accuracy. This paper uses a large number of photovoltaic power station data for experimental verification. The results show that this combined model has high accuracy and generalization ability.
Highlights
In recent years, with the increasing awareness of human beings to protect the environment and the continuous development of science and technology, all kinds of clean energy have been gradually paid attention to[1]
This paper proposes a combination prediction method that uses the K-means clustering algorithm to classify weather types according to meteorological factors and uses the Wavelet-Decomposition-Autoregressive Integrated Moving Average model (ARIMA) model and EDM-simulated annealing algorithm (SA)-Deep Belief Network (DBN) model to predict different types according to their characteristics
The instability of the photovoltaic power generation system will bring great security hidden trouble to the power grid when connected to the grid, so this paper combines meteorological data to classify the daily types by clustering method and selects the best prediction models of each day type
Summary
With the increasing awareness of human beings to protect the environment and the continuous development of science and technology, all kinds of clean energy have been gradually paid attention to[1]. Statistical methods and other types of artificial intelligence algorithms are simpler to model and have better prediction accuracy, a large amount of historical data is needed for learning and training[5]. From the available photovoltaic output data, it can be seen that the fluctuation range is very large due to weather factors, so the traditional method using a single prediction model can’t predict well in all weather conditions. A certain method will be more suitable for a specific weather condition, after the classification of weather, for the data type with large fluctuation amplitude, we should find a way to increase its stability so that the accuracy of the prediction model can be improved. This paper proposes a combination prediction method that uses the K-means clustering algorithm to classify weather types according to meteorological factors and uses the Wavelet-Decomposition-ARIMA model and EDM-SA-DBN model to predict different types according to their characteristics
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