Distributed Smart Device for Monitoring, Control and Management of Electric Loads in Domotic Environments

Morales, Rafael; Badesa, Francisco J.; García-Aracil, Nicolás; Pérez-Vidal, Carlos; Sabater, J. M.

doi:10.3390/s120505212

Cited by 23 publications

(12 citation statements)

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“…It has two inputs: one is proportional to the load voltage, and the other one is proportional to the current that circulates through the load. Its operation consists of digitalizing the signals which are related to the load voltage and current and multiplying them so that the result is proportional to the load power [46,47].…”

Section: Architecture Of the Monitoring System Materials Used For Thmentioning

confidence: 99%

Development of a Prototype for Monitoring Photovoltaic Self-Consumption Systems

et al. 2020

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Currently, the increasing energy consumption around the world and the environmental impact resulting from the use of fossil fuel-based energy have promoted the use of renewable energy sources such as photovoltaic solar energy. The main characteristic of this type of energy is its unpredictability, as it depends on meteorological conditions. In this sense, monitoring the power generation of photovoltaic systems (PVS) in order to analyze their performance is becoming crucial. The purpose of this paper is to design a monitoring system for a residential photovoltaic self-consumption system which employs an Internet of Things (IoT) platform to estimate the photovoltaic power generation according to solar radiation and temperature. The architecture of the developed prototype will be described and the benefits of providing the use of IoT for monitoring will be highlighted, since all data collected by the data acquisition system (DAS) may be stored in the Cloud. The comparison of the results with those of other monitoring systems was very positive, with an uncertainty that complies with the IEC61724 standard.

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Section: Architecture Of the Monitoring System Materials Used For Thmentioning

confidence: 99%

Development of a Prototype for Monitoring Photovoltaic Self-Consumption Systems

et al. 2020

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“…Furthermore, high-power devices, e.g., stoves or air conditioning units, are generally connected to dedicated circuits, and thus more sensitive sensors can be employed for monitoring devices with lower power draw. The deployment of metering equipment in individual wall outlets, as presented in [14], [15], is the next logical step towards the completely distributed deployment of sensing devices.…”

Section: Related Workmentioning

confidence: 99%

Electric appliance classification based on distributed high resolution current sensing

Reinhardt

Burkhardt

Zaheer

et al. 2012

37th Annual IEEE Conference on Local Computer Networks -- Workshops

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Today's solutions to inform residents about their electricity consumption are mostly confined to displaying aggregate readings collected at meter level. A reliable identification of appliances that require disproportionate amounts of energy for their operation is generally unsupported by these systems, or at least requires significant manual configuration efforts. We address this challenge by placing low-cost measurement and actuation units into the mains connection of appliances. The distributed sensors capture the current flow of individual appliances at a sampling rate of 1.6kHz and apply local signal processing to the readings in order to extract characteristic fingerprints. These fingerprints are communicated wirelessly to the evaluation server, thus keeping the required airtime and energy demand of the transmission low. The evaluation server employs machine learning techniques and caters for the actual classification of attached electric appliances based on their fingerprints, enabling the correlation of consumption data and the appliance identity. Our evaluation is based on more than 3,000 current consumption fingerprints, which we have captured for a range of household appliances. The results indicate that a high accuracy is achieved when locally extracted current consumption fingerprints are used to classify appliances.

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“…The application of domotic systems allows the adequate management of the consumption of energy. These are several of the systems that can be found on a university campus, and that allow the supposition that the transformation to a smart campus is feasible [10]. The IoT concept provides the necessary support for all devices to connect to the Internet, and the data they generate is stored in centralized places such as the cloud.…”

Section: Introductionmentioning

confidence: 99%

Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus

2019

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Currently, the integration of technologies such as the Internet of Things and big data seeks to cover the needs of an increasingly demanding society that consumes more resources. The massification of these technologies fosters the transformation of cities into smart cities. Smart cities improve the comfort of people in areas such as security, mobility, energy consumption and so forth. However, this transformation requires a high investment in both socioeconomic and technical resources. To make the most of the resources, it is important to make prototypes capable of simulating urban environments and for the results to set the standard for implementation in real environments. The search for an environment that represents the socioeconomic organization of a city led us to consider universities as a perfect environment for small-scale testing. The proposal integrates these technologies in a traditional university campus, mainly through the acquisition of data through the Internet of Things, the centralization of data in proprietary infrastructure and the use of big data for the management and analysis of data. The mechanisms of distributed and multilevel analysis proposed here could be a powerful starting point to find a reliable and efficient solution for the implementation of an intelligent environment based on sustainability.

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Distributed Smart Device for Monitoring, Control and Management of Electric Loads in Domotic Environments

Cited by 23 publications

References 10 publications

Development of a Prototype for Monitoring Photovoltaic Self-Consumption Systems

Development of a Prototype for Monitoring Photovoltaic Self-Consumption Systems

Electric appliance classification based on distributed high resolution current sensing

Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus

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