Abstract
The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.
Highlights
With the development of today's society, energy shortages and environmental pollution have become increasingly prominent global problems
The excessive development and utilization of traditional energy sources such as oil and coal have led to a series of problems such as deterioration of environmental quality, global warming, severe energy shortages, and even the outbreak of energy wars, which have become a focus of attention.In recent years, through modern control, artificial intelligence, and other theories, some more practical and convenient photovoltaic power prediction methods have been published
Literature[1]The wavelet analysis method is used to decompose the NWP meteorological variables, and the decomposed sub-sequences are used to train the particle swarm to optimize the support vector machine. When using this prediction method to select various meteorological factors, the influence of the meteorological factors on the photovoltaic power plant's output power ignores the difference in impact
Summary
With the development of today's society, energy shortages and environmental pollution have become increasingly prominent global problems. Literature[1]The wavelet analysis method is used to decompose the NWP meteorological variables, and the decomposed sub-sequences are used to train the particle swarm to optimize the support vector machine. When using this prediction method to select various meteorological factors, the influence of the meteorological factors on the photovoltaic power plant's output power ignores the difference in impact. The main research content of this article is based on the in-depth study of the principle of photovoltaic power prediction. It proposes a photovoltaic power prediction method based on the clustering algorithm. The results are compared according to national evaluation standards, and the optimal prediction model is obtained
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