Prediction method of power market bidding data based on least square method

Abstract

The invention discloses a prediction method of power market bidding data based on a least square method. The prediction method comprises a step of obtaining an average transaction price historical data, establishing a time and average price prediction model, calculating a residual sum of squares, determining parameters of a prediction equation, calculating a partial derivative of the residual sum of squares to the prediction equation, setting the partial derivative to be zero, obtaining parameter values, setting multiple power of a prediction equation independent variable, training data, and obtaining different residual sums of squares, a step of orderly circulating to obtaining residual sums of squares and comparing the residual sums of squares, taking a minimum value of the residual sums of squares, and obtaining a precise prediction equation, a step of training more real data based on various prediction equations and obtain an optimal solution, and a step of orderly setting the multiple prediction equations to match data, and finally obtaining a prediction equation with a highest goodness of fit.

Description

technical field [0001] The invention belongs to the field of electric power market, and relates to a method for predicting bidding transaction data in the electric power market based on the least square method. Background technique [0002] Due to the introduction of the competition mechanism in the electricity market, there are many uncertain factors in the electricity market. Enterprises must accurately and timely understand the current production cost situation, and at the same time fully understand the market supply and demand information, and adopt the most effective bidding strategy based on this information, and the bidding strategy generates It is necessary to derive different bidding strategies based on some basic data such as marginal cost of power plants, bidding price, transaction volume, and market clearing price. Therefore, modern machine learning research is used to realize the prediction of power market bidding transaction data, and let the real data Reaching...

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Problems solved by technology

[0006] In recent years, the research on load forecasting in this field based on the least squares method has become popular, but compared with electricity price forecasting, the difficulty is different. The electricity price forecast of bidding transaction has many uncertain factors, it has a non-growth trend and some related human factors , it is difficult to express mathematically. If only one prediction equation is limited to train the data and then fit, it is difficult to predict the data of the change curve

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