Smart meter monitoring and data mining techniques for predicting refrigeration system performance
Expert Systems with Applications: An International Journal
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In order to efficiently improve the prediction accuracy by selecting input variables and the training pattern,a load forecasting model based on data mining technique is presented. The model consists of three stages:firstly,the rough set theory and the genetic algorithm are applied to find relevant factors to the load;secondly,the active selection of the training pattern is carried out; last, the artificial neural network is used to predict load. Testing results on a real power system show that the proposed model is promising for load forecasting and is more accurate than the traditional one.