Non-Intrusive Load Monitoring (NILM), also referred to as energy disaggre- gation, is a promising technique to extract appliance-level information (such as heating, cooling, refrigeration, and lighting) from aggregated building-level metering data. NILM has the potential to be a cost-effective way to improve en- ergy efficiency and reduce energy waste as sub-metering individual appliances is expensive and inconvenient. A number of energy disaggregation techniques have been studied in the past, but to achieve a high prediction accuracy, NILM requires measuring both real and reactive powers. Metering both real and re- active power can be expensive and often needs additional data storage. In this thesis, we addressed this gap by introducing an algorithm called Gaussian Pro- cess Change Surface (GPCS) to energy disaggregation in that GPCS only re- quires real power compared to other NILM algorithms. A novel approach was to model the aggregate power and the background appliances as two indepen- dent Gaussian Processes, and the target appliance as the difference between the aggregate and the background. In addition, we developed Gaussian Process, Deep Kernel Learning, and other existing state-of-the-art techniques as compar- isons to the GPCS. We tested the effectiveness of this new method on two in- dependent data sets. Our evaluation demonstrated that GPCS can achieve high prediction accuracy while eliminating the need to use reactive power compared to the existing NILM algorithms.