http://hdl.handle.net/10256/1523 Mon, 25 Aug 2025 02:24:19 GMT 2025-08-25T02:24:19Z http://hdl.handle.net/10256/27129 States changes representation for time series García Pavioni, Alihuén ENG- The exponential growth in wearable device usage has generated vast amounts of time series data, presenting opportunities for various applications like activity recognition and health monitoring. However, analyzing these datasets poses challenges due to their complexity and length. To address this, this doctoral thesis proposes the State Changes Representation for Time Series (SCRTS), a method aimed at extracting relevant information related to the dynamics of the time series while significantly reducing the dimensionality. Moreover, SCRTS is length-independent, enabling the application of this algorithm to frames (consecutive values of the variables in the time series related to a given class) of varying lengths while producing feature vectors of the same size. This aspect is crucial for classifications, ensuring uniformity in feature representations across different time series lengths. The SCRTS algorithm is presented in two variants: the one-dimensional (1D-SCRTS) and the multidimensional (mD-SCRTS) approaches. In the 1D-SCRTS, each frame is represented by a sequence of states derived from vector magnitudes, which summarize the information of the interrelated variables at each time point of time series samples. In contrast, the mD-SCRTS considers individual variable values before discretization, allowing it to capture information related to all variable values independently. The effectiveness of the SCRTS is demonstrated through activity classification experiments using three accelerometer datasets. Both the 1D-SCRTS and the mD-SCRTS exhibit outstanding dimensionality reduction capabilities while achieving considerable classification performance; CAT- El creixement exponencial en l'ús de dispositius portàtils ha generat vastes quantitats de dades de sèries temporals, presentant oportunitats per a diverses aplicacions com el reconeixement d'activitats i el monitoratge de la salut. No obstant això, analitzar aquests conjunts de dades presenta reptes a causa de la seva complexitat i longitud. Per abordar això, aquesta tesi doctoral proposa la Representació de Canvis d'Estat per a Sèries Temporals (SCRTS, per les seves sigles en anglès), un mètode destinat a extreure informació rellevant relacionada amb la dinàmica de la sèrie temporal mentre es redueix significativament la dimensionalitat. A més, el SCRTS és independent de la longitud, la qual cosa permet l'aplicació d'aquest algoritme a marcs (valors consecutius de les variables en la sèrie temporal relacionada amb una classe determinada) de diferents longituds mentre es produeixen vectors de característiques del mateix tamany. Aquest aspecte és crucial per a les classificacions, assegurant uniformitat en les representacions de característiques per a marcs de diferent longitud. L'algoritme SCRTS es presenta en dues variants: l'enfocament unidimensional (1D-SCRTS) i el multidimensional (mD-SCRTS). En el 1D-SCRTS, cada marc està representat per una seqüència d'estats derivada de magnituds vectorials que resumeixen la informació de les variables interrelacionades en cada moment del temps de les mostres de la sèrie temporal. En contrast, el mD-SCRTS considera els valors de les variables de les mostres de forma individual abans de la discretització, el que li permet capturar informació relacionada amb tots els valors de les variables de la mostra de forma independent. L'efectivitat del SCRTS es demostra a través d'experiments de classificació d'activitats utilitzant tres conjunts de dades d'acceleròmetres. Tant el 1D-SCRTS com el mD-SCRTS exhibeixen capacitats sobresortints de reducció de dimensionalitat mentre aconsegueixen un rendiment de classificació considerable Tue, 10 Sep 2024 00:00:00 GMT http://hdl.handle.net/10256/27129 2024-09-10T00:00:00Z http://hdl.handle.net/10256/26702 Methodological Advances in temperature dynamics modeling for energy-efficient indoor air management systems Iglesias i Cels, Ferran; Massana i Raurich, Joaquim; Burgas Nadal, Llorenç; Planellas Fargas, Narcís; Colomer Llinàs, Joan Heating, ventilation, and air conditioning (HVAC) systems account for up to 40% of the total energy consumption in buildings. Improving the modeling of HVAC components is necessary to optimize energy efficiency, maintain indoor thermal comfort, and reduce their carbon footprint. This work addresses the lack of a general methodology for data preprocessing by introducing a novel approach for feature extraction and feature selection based on physical equations and expert knowledge that can be applied to any data-driven model. The proposed framework enables the forecasting of indoor temperatures and the energy consumption of individual HVAC components. The methodology is validated with real-world data from a system involving a fan coil unit and a thermal inertia deposit powered by geothermal energy, achieving a coefficient of determination (R2) of 0.98 and mean absolute percentage error (MAPE) of 0.44% Sun, 13 Apr 2025 00:00:00 GMT http://hdl.handle.net/10256/26702 2025-04-13T00:00:00Z http://hdl.handle.net/10256/26644 Enabling charging point operators for participation in congestion markets Massana i Raurich, Joaquim; Burgas Nadal, Llorenç; Cañigueral Maurici, Marc; Sumper, Andreas; Meléndez i Frigola, Joaquim; Colomer Llinàs, Joan This paper explores the viability of electric vehicle charging point operators to act as flexibility service providers in local flexibility markets. The work focuses on the requirements for operating in local intra-day markets and specifically in solving grid congestion at the distribution level. The explored approach assumes an alternative to bilateral agreements constrained to the capacity of the charging point operator to forecast the electric vehicle demand and flexibility effectively. The current paper analyses the flexibility capacity and proposes a methodology to address the re-dispatch process within the GOPACS (The Netherlands) context.The flexibility estimation methodology comprises two forecasting steps: forecasting the aggregated flexibility capacity and forecasting electric vehicles flexibility. A detailed case study presents data from the real electric vehicle sessions in Amsterdam City. The experimental results validate the effectiveness of the proposed methodology, establishing a robust basis for further research Sun, 01 Jun 2025 00:00:00 GMT http://hdl.handle.net/10256/26644 2025-06-01T00:00:00Z http://hdl.handle.net/10256/26633 A Study of Using Custom-Clustering Algorithm for a New Treatment of COVID-19 Figueras Coma, Albert; Esteva Payet, Santiago; Rosa, Josep Lluís de la This work deals with the problem of knowing a group of people that adequately responds to a specific treatment in order to classify community in groups is the objective. In the process to make this classification, a lot of work is necessary to analyze the results from the cluster analysis and obtain the minimal parameters that define a specific group that can be classified as treatment target. Also is presented the algorithm called Custom-clustering to solve this problem Tue, 14 Jul 2020 00:00:00 GMT http://hdl.handle.net/10256/26633 2020-07-14T00:00:00Z