In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

In this study, we utilize the cross and partial correlation analyses in order to investigate the dependence of power energy consumption on the temperature. To this end, we use a time series data that consists of three attributes : an hourly measured electric power consumption, temperature, and humidity. We, in particular, divide the yearly data into monthly base, and estimate the cross correlation coefficients between all possible pairs of attributes for each monthly based data. We found that temperature and power consumption are negatively correlated in the winter; positively correlated in the summer. A similar trend was found between humid and power consumption. This implies that when temperature or humidity is relatively high or low, the power consumption increases due to the cooling and heating system at work. In contrast, the correlation between temperature and humid behaves differently from those between temperature and power consumption. These results can be used to effectively manage the power system.