A NonIntrusive Appliance Recognition System Depleting energy resources and the unstable supply of raw materials call for innovations in the energy industry, such as in energy generation, distribution, and management. Moreover, increasing electricity prices take a toll on consumers that operates within a strict budget. This study in particular focuses on the proper management and utilization of energy from the consumeru0027s perspective. The objective is to develop a nonintrusive appliance recognition system that can identify the appliances that are being used and calculate how much each of these appliances contribute to the total electricity consumption. Installation of the monitoring system, which utilizes a single sensor clamped to the main power line and used to measure the total energy consumption, does not alter the electrical system, thus, nonintrusive. With this system, the homeowner can monitor which appliances are in use and how much energy they consume. Also, this translates to savings for the household when data provided by the system lead to smarter energymanagement choices. For this study to be deployable in households, a dataacquisition system to streamline the datagathering procedure was needed. Also, a machine learning algorithm was trained and implemented to perform the appliance recognition task given input features from the frequency domain of the measured aggregate data from the main power line. Lastly, the system was tested for prediction accuracy and characterized; and then necessary optimizations were implemented.

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clusteringbased analysis of energy consumption to categorize the consumersxe2x80x99 electricity usage into different levels. First, a deep autoencoder that transfers the lowdimensional energy consumption data to highlevel representations was trained. Second, the highlevel representations were fed into an adaptive selforganizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.