Ultra-short-term prediction method of optical power with multi-dimensional isomorphic and heterogeneous bp neural network

Abstract

The invention discloses a multi-dimensional isomorphic heterogeneous BP neural network optical power ultrashort-term prediction method. The method specifically comprises the steps of: SS1, utilizing grid-connected active power measured data, meteorological substation measured data, grid-connected active power historical data, meteorological substation historical data and weather forecast data comprehensively, analyzing data segmented by the day, calculating an index of similarity under the conditions of approximate meteorological conditions and similar active power, and classifying the data according to the index to form historical data samples; SS2, correcting numerical weather information according to the weather forecast data and the meteorological substation measured data; SS3, matching the samples under the conditions of approximate meteorological conditions according to the corrected numerical weather information and classified historical data samples, and selecting the approximate samples as input training samples of an artificial neural network; SS4, carrying out input data normalization, training sample selection and predictive output for a BP neural network; and SS5, repeating the process from step SS1 to step SS4 when the prediction in the next time period starts.

Description

technical field [0001] The invention relates to a multi-dimensional homogeneous heterogeneous BP neural network optical power ultra-short-term prediction method, which belongs to the technical field of photovoltaic power generation power prediction. Background technique [0002] At present, the country is vigorously developing clean energy technology, and photovoltaic power generation is an important part. The characteristics of photovoltaic power generation are renewable and non-polluting. The limitation is that output fluctuates with weather conditions. After grid connection, it will have a great impact on the power grid, especially for large-scale centralized photovoltaic power plants. If the output of photovoltaic power generation can be predicted in advance, it will be convenient for power grid dispatching, reasonable arrangement and formulation of power generation plan, adjustment of output distribution, economic dispatch, etc. Therefore, accurate forecasting of photov...

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Problems solved by technology

The multi-dimensional homogeneous heterogeneous BP neural network optical power ultra-short-term prediction method proposed by the present invention adopts a method of unified analysis and classification of seasonal factors and meteorological environments, and selects matching sample libraries from the database for training, thereby avoiding the establishment of multiple neural networks. Network model, and the conduction factor is obtained from meteorological data training, which solves the problem of power generation output changing with meteorological conditions

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