Data-driven energy efficiency solutions are highly demanded and among the most important topics in the related fields, such as smart building/city design, applied energy applications, electrical and electronic engineering, automation and constructions. Three topics are covered in this seminar, including: 1) data-driven fault detection and diagnosis (FDD) of heating, ventilation and air-conditioning (HVAC) systems, 2) energy consumption forecasting problem for individual households and 3) solar energy performance and optimization solutions. First, I will introduce the most up-to-date total energy performance problems of the above three topics and the motivations of using data-driven and machine learning techniques to solve these problems. Second, the methodologies that we proposed and developed in the past five years will be briefly described, which include extensions of generative adversarial networks, parallel long-short term memory networks and the federated learning structure for smart building and energy optimization problems. Last, the trends of using machine learning technology in the field of smart city design are summarized. At the end of the seminar, I will introduce some of my teaching and service works in the National University of Singapore (NUS).