Electricity price prediction method based on similar days

Abstract

The invention discloses an electricity price prediction method based on similar days. The electricity price prediction method comprises the steps: obtaining a load prediction curve of a prediction region on a prediction day from a load prediction system; determining a first similar day set comprising M similar days based on a least square method according to the load prediction curve and a pre-established historical database, and determining a second similar day set comprising N similar days based on a correlation coefficient statistical algorithm; calculating a first weighting coefficient anda second weighting coefficient according to the effective historical load curve and the load prediction curve of the similar days in the first similar day set and the second similar day set; calculating a first electricity price prediction value and a second electricity price prediction value according to the first weighting coefficient, the second weighting coefficient and the effective historical electricity price curve; calculating an electricity price correction amount according to the first electricity price prediction value and the second electricity price prediction value; and according to the electricity price correction amount and the second electricity price prediction value, calculating to obtain a standard electricity price prediction value of the prediction day. The electricity price prediction method can improve the accuracy of the electricity price prediction value.

Description

technical field [0001] The invention relates to the technical field of power system economic operation, in particular to a method for predicting electricity prices based on similar days. Background technique [0002] In the electricity market environment, for the system power purchaser, the marginal power price constitutes its unit power purchase cost, and the forecast of the marginal power price makes its own dynamic cost control possible; , Stable, orderly competition and development provide a scientific basis; for power generation, the marginal price of electricity is its product price, and its profit depends on a successful quotation strategy. The basis for the formation of a quotation strategy is to accurately grasp the short-term market trend The key is an accurate forecast of the marginal electricity price. Therefore, it is especially important to predict the marginal electricity price accurately in advance for the good development of the electricity market. [0003...

AI Technical Summary

Problems solved by technology

The time series method regards the fluctuation of short-term electricity price as a random process, and determines the specific electricity price prediction formula through pattern recognition and parameter estimation; The fluctuation is different from the fluctuation of the load, the change curve of the two days before and after may be very different, and the accuracy is not high

The similar day method is to find the historical days similar to the predicted daily load curve, and weight the electricity price in the corresponding period of these historical days to obtain the forecasted electricity price; the traditional similar day method in the prior art is simple in principle and easy to implement, but often only through A single similarity algorithm is used to obtain similar days, and the weighted processing of historical data is used to obtain electricity price forecasts, but the accuracy is still not high

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