Short-period electricity price prediction method based on BP neural network and Markov chain

Abstract

The invention relates to a short-period electricity price prediction method based on a BP neural network and a Markov chain. The method comprises the following steps of (S1), establishing a BP neural network model for predicting a short-period electricity price and performing preliminary prediction, thereby obtaining a predicted electricity price; (S2), performing error analysis on the predicted electricity price obtained in the step (S1), and calculating an absolute percentage error APE; (S3), dividing the Markov state space of the APE by means of a fuzzy C-means algorithm, and obtaining the Markov chain state of the APE; (S4), according to the Markov chain state of the APE, calculating a Markov state transition probability matrix; (S5), according to the Markov state transition probability matrix, calculating an APE state probability vector at a predicated time; and (S6), correcting the predicted electricity price and obtaining a final prediction result. Compared with the prior art, the short-period electricity price prediction method has advantages such as high precision.

Description

technical field [0001] The invention relates to a short-term electricity price prediction method of a power grid, in particular to a short-term electricity price prediction method based on a BP neural network and a Markov chain. Background technique [0002] Short-term electricity price forecasting is mainly to predict the electricity price in the next few hours, days or a week. Knowing the future trend of electricity prices, market participants can formulate an appropriate bidding strategy, so as to obtain stable returns while bidding on risks. Power users can also arrange reasonable power consumption time slots, thereby reducing power consumption costs. [0003] At present, the artificial neural network model is widely used in electricity price forecasting. Most of the literature uses BP neural network model for short-term forecasting of electricity price, which provides ideas for the application of neural network in electricity price forecasting. However, from the pred...

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Problems solved by technology

However, from the prediction results of these literatures, it can be seen that although the BP neural network model has strong nonlinear mapping ability and good generalization characteristics, it is highly dependent on samples, and the accuracy is not high enough when predicting short-term electricity prices with drastic changes. Well hit the forecast target

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