An Overview: the Development of Prediction Technology of Wind and Photovoltaic Power Generation

The energy management information system has became a research hotspot with the rapid development of smart grid, which using for the integration of micro-grid and traditional electric power grid. However, renewable energy sources (such as wind energy, tidal energy, etc.) with unstable, intermittent and controllability characteristics bring a number of challenges to the integration of micro-grid and traditional electric power grid. Solving these problems depend on accurately forecast micro-grid power generation output in a certain time. This article outlines and tracks the main prediction technologies of wind and photovoltaic power generation over the past 10 years, and highlights these prediction models based on statistics (such as Kalman filtering, data mining and wavelet transform, etc.) and artificial intelligence (such as neural networks, fuzzy inference and biological intelligence algorithm, etc.). Finally, this paper also pointed out the shortcomings and improved directions of various forecasting techniques to help researchers in related fields propose better prediction model of power generation forecast.