SUPPRESS Research Group

Leon, Spain

SUPPRESS Research Group

Leon, Spain
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Barrientos P.,SUPPRESS Research Group | del Canto C.J.,SUPPRESS Research Group | Moran A.,SUPPRESS Research Group | Alonso S.,SUPPRESS Research Group | And 3 more authors.
Communications in Computer and Information Science | Year: 2013

The highest cause of energy consumption in buildings is due to 'Heating, Ventilation, and Air Conditioning' (HVAC) systems. However, a large number of interconnected variables are involved in the control of these systems, so conventional analysis approaches are difficult. For that reason, data analysis by means of dimensionality reduction techniques can be a useful approach to address energy efficiency in buildings. In this paper, a method is proposed to visualize the relevant features of a heating system and its behavior and to help finding correlations between temporal, production and distribution variables. It uses a modification of the self-organizing map. The proposed approach is applied to a real building at the University of León. © Springer-Verlag Berlin Heidelberg 2013.


Dominguez M.,SUPPRESS Research Group | Alonso S.,SUPPRESS Research Group | Moran A.,SUPPRESS Research Group | Prada M.A.,SUPPRESS Research Group | Fuertes J.J.,SUPPRESS Research Group
Information Sciences | Year: 2015

The highest cause of energy consumption in buildings is due to Heating, Ventilation, and Air Conditioning (HVAC) systems. A large number of interconnected variables are involved in the control of these systems, so conventional analysis approaches are often difficult. For that reason, data analysis by means of dimensionality reduction techniques can be a useful approach to address energy efficiency in buildings. In this paper, a method is proposed to visualize the relevant features of a heating system and its behavior and to help finding correlations between temporal, production and distribution variables. For that purpose, a modification of the self-organizing map is used. The energy consumption of HVAC systems is also analyzed using a dimensionality reduction technique (t-Distributed Stochastic Neighbor Embedding, t-SNE). The proposed approach is applied to a real building at the University of León. © 2014 Elsevier Inc. All rights reserved.


Dominguez M.,SUPPRESS Research Group | Alonso S.,SUPPRESS Research Group | Fuertes J.J.,SUPPRESS Research Group | Prada M.A.,SUPPRESS Research Group | And 2 more authors.
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2014

The remote laboratories are proven tools for technological training. In these laboratories, the students interact with a real system through the Internet, as if they were physically in front of the system. When a remote laboratory is developed, many technical difficulties arefound, mainly with respect to the links between the different elements such as physical system, database and clients. In this sense, it is necessary to make an effort to standardize the implementation of the links. In this paper, we propose a standard application to communicate the physical systems and the database. This middleware, called OPC-DB, uses OPC (OLE for Process Control) for communication with control systems and has been developed in LabVIEW. The software can be easily reused in different laboratories by means of a database. © IFAC.


Moran A.,SUPPRESS Research Group | Prada M.A.,SUPPRESS Research Group | Alonso S.,SUPPRESS Research Group | Barrientos P.,SUPPRESS Research Group | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Many preprocessing and prediction techniques have been used for large-scale electricity load forecasting. However, small-scale prediction, such as in the case of public buildings, has received little attention. This field presents certain specific features. The most distinctive one is that consumption is extremely influenced by the activity in the building. For that reason, a suitable approach to predict the next 24-hour consumption profiles is presented in this paper. First, the features that influence the consumption are processed and selected. These environmental variables are used to cluster the consumption profiles in subsets of similar behavior using neural gas. A direct forecasting approach based on Support Vector Regression (SVR) is applied to each cluster to enhance the prediction. The input vector is selected from a set of past values. The approach is validated on teaching and research buildings at the University of León. © Springer-Verlag Berlin Heidelberg 2012.


Moran A.,SUPPRESS Research Group | Fuertes J.J.,SUPPRESS Research Group | Prada M.A.,SUPPRESS Research Group | Alonso S.,SUPPRESS Research Group | And 2 more authors.
Communications in Computer and Information Science | Year: 2012

The analysis of the daily electricity consumption profile of a building and its correlation with environmental factors make it possible to estimate its electricity demand. As an alternative to the traditional correlation analysis, a new approach is proposed to provide a detailed and visual analysis of the correlations between consumption and environmental variables. Since consumption profiles are normally characterized by many electrical variables, i.e., a high dimensional space, it is necessary to apply dimensionality reduction techniques that enable a projection of these data onto an easily interpretable 2D space. In this paper, several dimensionality reduction techniques are compared in order to determine the most appropriate one for the stated purpose. Later, the proposed approach uses the chosen algorithm to analyze the profiles of two public buildings located at the University of León. © Springer-Verlag Berlin Heidelberg 2012.

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