Assessment of Temperature Sensitivity Analysis and Temperature Regression Model for Predicting Seasonal Bank Load Patterns

Abstract

The aim of this paper is to investigate the potential of air conditioning load management by solve the temperature regression model of load patterns for Banks and the temperature sensitivity depends on temperature change. The load survey system has been applied to record the Bank load of sampling Banks in Korea power system. To analyze the impact of temperature rise to the Bank load data, we executed statistic polynomial regression and the temperature sensitivity analysis on the Bank load data. Before that, we applied data preprocessing to make the data clear. It found that the week time is more sensitive than weekend and when the temperature is less deviated from the main tendency, the regression model can predict the load patterns with higher accuracy.