Power maximum load small-sample prediction method

Abstract

The invention provides a power maximum load small-sample prediction method. The method comprises the steps of a, acquiring the historical data of the annual maximum load of a power grid, and acquiring a training sample and a test sample; b, defining an optimization problem, and initializing the parameters of the quantum search algorithm and the harmony search algorithm; c, initializing a quantum library and a harmony library; d, generating a new solution in the harmony library at the HMCR and disturbing the new solution; e, evaluating the new solution and updating the harmony vector; f, determining the optimal solutions of model parameters; g, taking the optimal value of sigma and the optimal value of C into a least squares support vector machine (LSSVM) model, and training the model by utilizing the training sample and the test sample; h, predicting the annual maximum load of the power grid by utilizing the trained LSSVM model. According to the technical scheme of the invention, the small-sample prediction on the annual maximum load of the power grid is realized by means of the LSSVM model. At the same time, the optimal value of sigma and the optimal value of C for the LSSVM model are found out based on the quantum search algorithm and the harmony search algorithm. Therefore, the blindness of parameter selection is avoided effectively, and the prediction precision is greatly improved.

Description

technical field [0001] The invention relates to a method for predicting the annual maximum load of a power grid by using a small sample, and belongs to the technical field of power transmission and distribution. Background technique [0002] The annual maximum load forecast of the power grid is the basis of power system planning and economic operation. With the continuous development of the power industry, the accuracy of load forecasting is required to be higher and higher. Load forecasting-related models can be divided into three categories: classical forecasting models, metering-related forecasting models, and intelligent technology-related forecasting models. The classic forecasting model is simple to calculate, but the forecasting error is relatively high; the calculation process of forecasting methods based on artificial intelligence and measurement-related is relatively complicated, the meaning is not clear, and all require large data samples in order to make a scien...

AI Technical Summary

Problems solved by technology

The problem with the LSSVM model is that the kernel function width σ and the error penalty factor C have a greater impact on the learning and generalization capabilities of LSSVM, and the prediction accuracy depends on the reasonable choice of parameters.

However, so far there is no similar prediction method

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