Detailed information about load behavior and home's occupancy is important to implement Home Energy Management Systems (HEMS) capable of reducing energy consumption while maintaining user comfort. This is why Non-intrusive Appliance Load Monitoring (NIALM) and Non-intrusive Occupancy Monitoring (NIOM) have an important role to play in the new context of smart grid. This paper shows the implementation of two algorithms for transient event detection and classification, which is the first key step of a NIOM process. The first method employs Wavelet transform for the feature extraction and Artificial Neural Networks for the classification problem. The second method is based on the theory of Matched Filters to achieve the transient event detection and classification. Experiments permitted to validate the proposed methods using a dataset of occupied residential building.