Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining

In this article, a building electric energy prediction model using a Kriging method was developed for an efficient building energy management system (BEMS). In the prediction model, only easily obtainable weather factors such as temperature, humidity, wind speed, etc. were used as input parameters for actual application to the BEMS. In order to identify the effects of weather factors on building energy consumption, two data mining techniques were used: Analysis Of Variance (ANOVA) and Self-Organizing Map (SOM). The accuracy of the model using only easily obtain weather factors was compared with that of the model using the weather factors selected based on the results of data mining. According to the results, the building electric energy prediction model using only easily obtainable weather factors has sufficient predictive ability for BEMS. The developed building electric energy prediction model was applied to the optimization problem of charge/discharge scheduling for an electric energy storage system. The results showed that the building electric energy prediction model has sufficient accuracy for application to the BEMS.

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