Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms.

In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants.

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.