Load monitoring is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load monitoring system is to reduce power consumption by increasing consumers’ awareness of which appliances consume the most energy. Another example of an application of load monitoring is activity sensing in the home for the provision of healthcare services. This paper outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to define each appliance. These parameters are the steady-state current harmonics and the rate of change of the transient signal. Each appliance’s electrical characteristics are distinguishable using these parameters. There are three Types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first and then using a second classifier to identify individual appliances within these Types, the overall accuracy of the identification algorithm is improved.

Off grid telecom base stations in developing nations are powered by diesel generators. They are typically oversized and run at a fraction of their rated load for most of their operating lifetime. Running generators at partial load is inefficient and, over time, physically damages the engine. A hybrid configuration uses a battery bank, which powers the telecoms’ load for a portion of the time. The generator only operates when the battery bank needs to be charged. Adding a wind turbine further reduces the generator run hours and saves fuel. The generator is oblivious to the current wind conditions, which leads to simultaneous generator-wind power production. As the batteries become charged by the generator, the wind turbine controller is forced to dump surplus power as heat through a resistive load. This paper details how the relationship between barometric pressure and wind speed can be used to add intelligence to the battery charger. A Simulink model of the system is developed to test the different battery charging configurations. This paper demonstrates that if the battery charger is aware of upcoming wind conditions, it will provide modest fuel savings and reduce generator run hours in small-scale hybrid energy systems.