| Current electric electricity market reform is the world’s development trend ofpower industry and international scientific and engineering practice of electrical hotspots. In the electricity market environment, not only is the electricity market pricesignals of supply and demand, also control the levers of economic power markettransactions. Therefore, How to determine the reasonable needs of the market priceaccordingly can directly affect the normal operation of the electricity market, how theelectricity market based on historical data related to correct prediction of the futuremarket price, for all the market participants are of great significance.Electricity market in the power price is the most essential part of the relevanthealth. This paper briefly describes the composition of electricity power systems,characteristics and influencing factors, more commonly used on the current predictionmethods are discussed the advantages and disadvantages. In neural network theory, andits advantages in price forecasting based on the analysis, neural network based approachto price forecasting.The purpose of the proposed method to improve the accuracy of electricity priceforecasting, the use of rough set and neural network short-term price forecast wasconstructed hybrid multi-index model, and give the corresponding algorithm. Thisarticle describes the method of rough set and neural network methods, decision-makingsystem based on rough set view on the short-term price forecasting model formulti-index selection index, and index information for the model contains manyfeatures, the rough set method and the BP network method phase Combination. Datathrough the PJM power market, the use of MATLAB neural network toolbox designedBP network method and steps for verification. The results from this paper, the methodprovides a more accurate and effective forecasting, which bid for the right strategy andmarket participants in the electricity market risk management tools are great help. |
