A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques

[Abstract ]:This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.Junta de Castilla y León; LE078G18. UXXI2018/000149. U-220.Ministerio de Economía, Industria y Competitividad; DPI2016-79960-C3-2-

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.info:eu-repo/semantics/publishedVersio

Heterogeneous Collaborative Sensor Network for Electrical Management of an Automated House with PV Energy

In this paper we present a heterogeneous collaborative sensor network for electrical management in the residential sector. Improving demand-side management is very important in distributed energy generation applications. Sensing and control are the foundations of the “Smart Grid” which is the future of large-scale energy management. The system presented in this paper has been developed on a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead-acid batteries, controllable appliances and smart metering. Therefore, there is a large number of energy variables to be monitored that allow us to precisely manage the energy performance of the house by means of collaborative sensors. The experimental results, performed on a real house, demonstrate the feasibility of the proposed collaborative system to reduce the consumption of electrical power and to increase energy efficiency

Clustering Techniques Selection for a Hybrid Regression Model: A Case Study Based on a Solar Thermal System

[EN] This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks. A real dataset from a bio-climatic house named Sotavento placed on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has been collected. Authors have chosen the thermal solar generation system in order to study how works applying several cluster methods followed by a regression technique to predict the output temperature of the system. With the objective of defining the quality of each clustering method two possible solutions have been implemented. The first one is based on three unsupervised learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin) while the second one, employs the most common error measurements for a regression algorithm such as Multi Layer Perceptron.S

Sensors: New Challenges in Spain

The main goal of this special issue was to explore sensor technology and its applications in Spain. It is well-known that a reciprocal interrelation exists between sensor technology and the demand for solutions to different problems. Indeed, when a new sensor is developed, it offers a solution to a problem, but also if a problem requires a solution perhaps new sensors or technologies based on existing sensors could be developed. [...

IoT and iTV for interconnection, monitoring, and automation of common areas of residents

Internet y, en particular, las redes de computadoras se han convertido en un problema clave en nuestra vida cotidiana, debido al nacimiento de las tecnologías inalámbricas. Internet of Things (IoT) tiene como objetivo integrar sensores y actuadores en objetos diarios, maximizando la miniaturización y minimizando el costo económico de estos componentes de hardware. El propósito es conectar estos componentes a Internet a través de redes inalámbricas y fijas, y así producir información en tiempo real que luego se almacena para su procesamiento posterior. Por otro lado, la televisión interactiva (iTV) combina la televisión tradicional con una interactividad similar a la de Internet y la computadora personal. La evolución de la tecnología de TV ha llevado la potencia informática a este dispositivo, ofreciendo servicios aparte de los tradicionales, lo que lo convierte en un dispositivo capaz de ejecutar aplicaciones y maximizar su potencial de red. Este trabajo presenta un marco que incluye e integra una red de sensores inalámbricos, una plataforma IoT y una aplicación de TV interactiva real. Cubre el despliegue y la comunicación de la red de sensores inalámbricos a través de la interoperabilidad de los datos, hasta el consumo final, a través de una aplicación de televisión interactiva real. Se ha probado dentro de una comunidad residencial para proporcionar información en tiempo real, con el fin de mejorar la calidad de vida de sus habitantes. Además, incorpora la posibilidad de analizar esta información para establecer procesos con el objetivo de reducir el consumo de energía, mejorando así la sostenibilidad y contribuyendo al uso eficiente de los recursos existentes. El marco propuesto sirve como base para cualquier despliegue de características similares.Internet and, in particular, computer networks have become a key issue in our daily lives, due to the birth of wireless technologies. Internet of Things (IoT) aims to integrate sensors and actuators in daily objects, maximizing miniaturization and minimizing the economic cost of these hardware components. The purpose is to connect these components to the Internet through wireless and fixed networks, and thereby produce information in real time which is then stored for later processing. On the other hand, Interactive TV (iTV) combines traditional TV with interactivity similar to that of the Internet and the personal computer. The evolution of TV technology has brought computing power to this device, offering services apart from the traditional ones, making it a device capable of running applications and maximizing its networking potential. This work presents a framework that includes and integrates a wireless sensor network, an IoT platform, and a real interactive TV application. It covers the deployment and communication of the wireless sensor network through the interoperability of data, to final consumption, through a real interactive television application. It has been tested within a residential community to provide real-time information, in order to improve the quality of life of its inhabitants. In addition, it incorporates the possibility of analyzing this information to establish processes with the objective of reducing energy consumption, thus improving sustainability and contributing to the efficient use of existing resources. The proposed framework serves as the basis for any deployment of similar characteristics.peerReviewe

Neural network controller for active demand side management with PV energy in the residential sector

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation

A Semi-Distributed Electric Demand-Side Management System with PV Generation for Self-Consumption Enhancement

This paper presents the operation of an Electrical Demand-Side Management (EDSM) system in a real solar house. The use of EDSM is one of the most important action lines to improve the grid electrical efficiency. The combination between the EDSM and the PV generation performs a new control level in the local electric behavior and allows new energy possibilities. The solar house used as test-bed for the EDSM system owns a PV generator, a lead-acid battery storage system and a grid connection. The electrical appliances are controllable from an embedded computer. The EDSM is implemented by a control system which schedules the tasks commanded by the user. By using the control system, we define the house energy policy and improve the energy behavior with regard to a selected energy criterion, self-consumption. The EDSM system favors self-consumption with regard to a standard user behavior and reduces the energy load from the grid