Non-Intrusive Load Monitoring in the OpenHAB Smart Home Framework
Non-intrusive load monitoring (NILM) is an approach to energy monitoring, where machine learning techniques are applied to data from a single energy meter to determine the energy consumption of each appliance connected to the local electric network. This thesis presents a real-time NILM solution for the smart home that relies on the home automation platform openHAB for live power readings. NILMTK – an open-source energy disaggregation toolkit – is used to break down the aggregate live energy consumption data to the appliance level in real time. The disaggregated power data are then sent back to openHAB, where they can be displayed to the user and enable further automation. Tests conducted at the University of Tartu Internet of Things and Smart Solutions laboratory indicate high recognition accuracy for appliances with a steady high energy demand (e.g. a space heater), while lower accuracy scores were reported for appliances with a fluctuating power demand (e.g. a coffee maker) and lowpowered appliances that only make up a small proportion of the total energy consumption (e.g. a light bulb).
Graduation Thesis language
Graduation Thesis type
Bachelor - Computer Science