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

One of the main challenges in smart city models is consumer behaviour, namely guiding the efforts to promote optimal use of energy in the dynamics of the developing cities, through lower energy consumption without impact on the comfort level. This relates to all energy vectors and can be done through different means. The uptake stage of smart metering and information and communication technologies (ICT) varies in different countries, creating the need for tailored innovative approaches in motivating behavioural change. This paper presents the work carried out within the joint research project ITCity (an ICT platform for sustainable energy ecosystem in smart cities) between the European Union and Latin American Countries. Cooperation of energy researchers and experts in ICT and application developers facilitates adoption of a multipurpose interdisciplinary approach. This work aims at providing the smart city and energy research and innovation community broader understanding of factors influencing adoption of energy efficiency technologies. Different ways of guiding optimal energy use are addressed elaborating on the building blocks for a smart city which consider load patterns based on high resolution, high reporting rate measurements, extensive consumer surveys, available communication solution analysis and end-user interaction with the ICT tool based on gamification principles.

La eficiencia energética es parte de los objetivos establecidos por los gobiernos de todo el mundo para reducir la huella energética y proporcionar un desarrollo sostenible para todos. El advenimiento de nuevas tecnologías que permiten el monitoreo y autocontrol del consumo eléctrico dentro de los hogares, como los medidores inteligentes, permiten a los usuarios finales integrarse en los sistemas de gestión inteligente de la red eléctrica, al proporcionar información sobre los perfiles de consumo de energía eléctrica y los precios de ésta. El presente trabajo realiza una clasificación de clientes residenciales a partir de los datos de consumo obtenidos desde medidores inteligentes. Para esto, se utiliza una metodología basada en el algoritmo K-means para identificar patrones de comportamiento en el consumo asociado a 1179 clientes conectados a redes eléctricas de distribución de baja tensión del sur de Chile equipados con medidores inteligentes. Los resultados fueron validados a través del uso de medidas de la teoría de los conjuntos aproximados. En particular, se caracterizan grupos conformados por medio de sus centroides, posibilitando la conversión de los grandes volúmenes de datos coleccionados en conocimientos útiles, lo que resulta en beneficio tanto para el propio cliente residencial como para la empresa distribuidora de energía eléctrica. Los resultados muestran que dos clústeres son los que mejor representan el conjunto de clientes. La oportunidad de recopilar datos de consumo en tiempo real mediante estos dispositivos, ofrece perspectivas para optimizar las políticas públicas y privadas sobre distribución de energía eléctrica.

The need to determine which wireless IoT technology, currently available on the market, will be able to support a sizeable simultaneous load of information, types of electrical emergency alarms, required bandwidths, delays-latency, area coverage, and economic cost to serve areas of 600 square kilometers of surface and several users of around two million homes. This article compares Sigfox, LoRa, NB-IoT, and WiMAX technologies as possible solutions for last-mile communications. For this purpose, a network of hundreds of smart meters has been simulated, each transmitting different types of information. In conclusion, it has been obtained that, for a context with the priority of alarms and a large amount of information transport in massive last-mile communications in smart grids and for electrical metering traffic, it is highly recommended that it be done through WiMAX, which in the evaluation shows the best result compared to the other technologies.

The efficient use of energy and its implementation in domestic automation systems is an essential requirement in Smart Cities. However, this requires appropriate measurement devices and an M2M control-communication alternative that offers real-time visualization of the information. This article proposes a prototype of the home electric power consumption management platform, as an advance in generating a model for future Smart Grid. Therefore, the research implements smart socket devices and a coordinator of the communication of a home area network between these measuring devices to have an intelligent control of electric power. As a result, real-time data of the defined electrical parameters have been obtained. This information has been stored in the Internet cloud, also allowing remotely programming and controlling these measurement devices. The present research contributes to generating a profile of total load consumption for residential users and allowing them to know and compare their real consumption with what was reported by the distributors.

The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO₂ reduction.