In the past decade, smart home environment research has found application in many areas, such as activity recognition, visualization, and automation. However, less attention has been paid to monitoring, analyzing, and predicting energy usage in smart homes, despite the fact that electricity consumption in homes has grown dramatically. In this paper, we extract the useful features from sensor data collected in the smart home environment and select the most significant features based on mRMR feature selection criterion, then utilize three machine learning algorithms to predict energy use given these features. To validate these algorithms, we use real sensor data collected from volunteers living in our smart apartment testbed. We compare the performance between alternative learning algorithms and analyze the results of two experiments performed in the smart home.