Short-term optical power prediction method based on time series similarity

Abstract

The invention discloses a short-term optical power prediction method based on time series similarity. First, photovoltaic power station meteorological data and equipment operation data which are acquired in real time are repaired, unreasonable values or missing data are filled, and then all photovoltaic arrays are divided into several categories through power data analysis. Power curves of each category of photovoltaic arrays under the similar meteorological condition at the same period of the last year are found out according to meteorological prediction data, and future power is predicted in a weighted average manner. By adoption of big data analysis, optical power prediction precision is improved by comprehensive utilization of historical data and meteorological prediction data, thereby facilitating stationary operation of the power grid.

Description

technical field [0001] The invention relates to a short-term optical power prediction method based on time series similarity, relates to the technical fields of electric power dispatch control and photovoltaic power station dispatch management, and is mainly applicable to photovoltaic power station and electric power dispatch department to predict and evaluate power generation. Background technique [0002] With the continuous deepening of my country's energy green strategy, light energy, as a renewable clean energy, has attracted more and more attention. In recent years, with the continuous improvement of photovoltaic equipment manufacturing technology, the price has been gradually reduced, and the power generation efficiency has been continuously improved. The efficiency of polycrystalline silicon cells is about 16% to 17%, and the efficiency of monocrystalline silicon cells is about 18% to 20%. According to the national plan, by 2020, the installed capacity of domestic ph...

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Problems solved by technology

The learning ability and generalization performance of the SVM prediction model are usually determined by the type and parameters of the kernel function. The SVM model using a single kernel function has certain limitations, and the prediction accuracy for more complex nonlinear data is low.

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