Pablo Barrientos

Analysis of electricity consumption profiles in public buildings with dimensionality reduction techniques

The analysis of the daily electricity consumption profile of a building and its correlation with environmental factors makes it possible to examine and 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 can be characterized by many components, the input space is high dimensional. For that reason, 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 tested in order to determine the most appropriate one for the stated purpose. Later, the proposed approach uses the chosen algorithm to analyze the influence of the environmental variables on the electricity consumption in public buildings located at the University of Leon. Finally, electricity profiles from all buildings are compared with regard to two aspects, the magnitude and dynamics of the consumption.

DIMENSIONALITY REDUCTION FOR DAMAGE DETECTION IN ENGINEERING STRUCTURES

The detection of damages in engineering structures by means of the changes in their vibration response is called structural health monitoring (SHM). It is a promising field but presents fundamental challenges. Accurate theoretical models of the structure are generally unfeasible, so data-based approaches are required. Indeed, only data from the undamaged condition are usually available, so the approach needs to be framed as novelty detection. Data are acquired from a network of sensors to measure local changes in the operating condition of the structures. In order to distinguish changes produced by damages from those caused by the environmental conditions, several physically meaningful features have been proposed, most of them in the frequency domain. Nevertheless, multiple measurement locations and the absence of a principled criterion to select among the potentially damage-sensitive features contribute to increase data dimensionality. Since high dimensionality affects the effectiveness of damage detection, we evaluate the effect of a dimensionality reduction approach in the diagnostic accuracy of damage detection.

OPC-DB Link for the Management of New Systems in a Remote Laboratory

Abstract 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.

Analysis of electricity bills using visual continuous maps

The information from the electricity bills of an institution such as the University of León, with several billing points, constitutes a high-dimensional data set which is quite complicated to visualize at a glance. The use of techniques for dimensionality reduction enables to obtain a two-dimensional representation of the original data set which highlights main features in data and is easier to visualize. If these techniques are combined with interpolation methods, the resulting continuous maps allow comparison and interpretation of a whole range of possible electric data sets, not only the original one. These tools allow us to generate interactive maps that can be used by untrained people to exploit and analyze the information in electricity bills, detect penalties due to a power demand excess or power factor decrease, and make decisions with regard to electricity contracts.

Two-stage approach for electricity consumption forecasting in public buildings

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 Leon.

MONITORING POWER CONSUMPTION USING A GENERALIZED VARIANT OF SELF-ORGANIZING MAP (SOM)

In this paper, we present a new approach for monitoring power consumption in several processes. The generalization of the envSOM algorithm, a variant of Self-Organizing Map (SOM), is used to build an electrical model and visualize the information. The envSOM extended to n hierarchical phases allows us to obtain a more accurate model from real past data. The model is conditioned hierarchically on environmental variables. In this way, time variables can be used to consider seasonality and weekday/hour periodicity. Time variable maps and electrical component planes make it possible to visualize and analyze power consumption. The representation of the Best Matching Unit (BMU) or its trajectory on these maps enables the on-line monitoring.

Remote Laboratory with an Electro-pneumatic Classification Cell

Abstract Remote laboratories are excellent educational tools that allow students and professors interact with real equipment used in automation projects. In this work, we present a new system, which consists of an electro-pneumatic classification cell. This cell includes a robot with six degrees of freedom, which has been added to our remote laboratory of automatic control (LRA-ULE). This system lets students work with many different industrial equipment such as PLCs, drives, sensors or actuators as well as to monitor a real industrial system. The architecture of the system is based on the three-layer architecture (physical system layer, server layer and client layer), also used in the rest of the platform. In addition, a process simulation and a set of practical tasks have been developed, so that students can work with the physical system through a webpage included in our remote laboratory.

Analysis of Electricity Consumption Profiles by Means of Dimensionality Reduction Techniques

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 Leon.

Improving User Interaction in Remote Laboratories through HTML5/AJAX

Abstract The field of remote and virtual laboratories for automatic control has been developed over the years. Recent technologies make it possible to address common challenges that need to be overcome in order to achieve flexibility, scalability and greater educational value. The client application is a key component of the laboratory, since students need interactivity and feedback. For that reason, in this paper, the joint use of HTML5 and AJAX is proposed to overcome some deficiencies present in the state-of-the-art client application technologies. Besides, a set of methods is proposed to solve usual challenges associated to the remote laboratories and facilitate the development and integration of HTML5-based client applications. The approach is tested with an existing remote laboratory to validate both its performance and the ability to integrate the proposed technology in a fast and easy way.

Analysis of Heating Systems in Buildings Using Self-Organizing Maps

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 Leon.