In this paper, we propose a method for forecasting power demand using meteorological data and human congestion information. In an energy management system (EMS), accurate power demand forecasts reduce the cost on the demand side and stabilize the power supply on the supply side. Although previously observed power consumption and meteorological data are conventionally used for forecasting power demand, it is difficult to estimate power demand in cases that are greatly affected by the behavior of people. Power consumption may vary according to the behavior of just one person, depending on the size of the community. In this study, the power demands of multiple buildings on the campus of a university are estimated accurately by analyzing heterogeneous data obtained with various sensors. Experiments show that using meteorological data and human congestion improves results. Consequently, we confirm that a cyber physical system can play an important role in the construction of an EMS.