<p>Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience, efficiency, and security of urban/building energy systems while safeguarding occupant comfort. However, data reflecting such behavior are often considered as outliers, and removed or smoothed during preprocessing, limiting insights into their potential impacts. This paper proposes an abnormal behavior analysis method that identifies outliers (considering data distribution) and anomalies (considering the physical context) based on the statistical principle and domain knowledge, assessing their effects on energy supply security. A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems. The method is illustrated by data from 1,451 buildings in a city. Results show that the proposed method can identify abnormal data effectively. Buildings in the primary industry have more outliers, while those in the tertiary industry have more anomalies. Seven buildings affecting both the security and economy of urban energy systems are identified. The outliers rise more frequently from 8:00 to 18:00, on weekdays and in the summer and winter months. However, the anomaly distribution has a weak connection with time. Moreover, the abnormal electricity usage behavior positively correlates with outdoor air temperatures. This method provides a new perspective for identifying potential risks, managing energy usage behavior, and enhancing flexibility of the urban energy systems.</p>