Abstract

For an efficient energy management by residential users, monitoring and control of connected household appliances is required. If the consumption pattern of each of these devices is identified, then the management will be more efficient, reducing both the billing and the CO2 emissions. This paper proposes a model for the recognition of energy consumption patterns in household appliances, based on the capture of electrical parameters in connected appliances, through Smart Socket with an Intrusive Load Monitoring approach. The data acquisition system corresponds to an-Internet of Things (IoT) platform that uses Automatic Meter Reading devices, connected to a IoT-gateway via Wi-Fi to send data to an application on the web. For the recognition of the patterns, machine learning techniques are used. Accuracy results on pattern identification are obtained about 91% after applying a backpropagation method in an Artificial Neural Network in time basis. Through this work, the prediction of consumer categories in household appliances, with high levels of reliability and under multiple operating states, is reached. These results enhance the efficient management of energy in a Smart Home and Smart Cities environment