Experimental characterization and implementation of an integrated autoregressive model to predict the thermal performance of vegetal façades

Experimental characterization and implementation of an integrated autoregressive model to predict the thermal performance of vegetal façades

Predictor paramétrico de glucosa subcutánea

La diabetes mellitus es una enfermedad que se caracteriza por la nula o insuficiente producción de insulina, o la resistencia del organismo a la misma. La insulina es una hormona que ayuda a que la glucosa (por ejemplo la obtenida a partir de los alimentos ingeridos) llegue a los tejidos periféricos y al sistema nervioso para suministrar energía. Hoy en día la tecnología actual permite abordar el desarrollo del llamado “páncreas endocrino artificial”, que consta de un sensor continuo de glucosa subcutánea, una bomba de infusión subcutánea de insulina y un algoritmo de control en lazo cerrado que calcule la dosis de insulina requerida por el paciente en cada momento, según la medida de glucosa obtenida por el sensor y según unos objetivos. El mayor problema que presentan los sistemas de control en lazo cerrado son los retardos, el sensor de glucosa subcutánea mide la glucosa del líquido intersticial, que representa la que hubo en la sangre un tiempo atrás, por tanto, un cambio en los niveles de glucosa en la sangre, debidos por ejemplo, a una ingesta, tardaría un tiempo en ser detectado por el sensor. Además, una dosis de insulina suministrada al paciente, tarda un tiempo aproximado de 20-30 minutos para la llegar a la sangre. Para evitar trabajar en la medida que sea posible con estos retardos, se intenta predecir cuál será el nivel de glucosa en un futuro próximo, para ello se utilizara un predictor de glucosa subcutánea, con la información disponible de glucosa e insulina. El objetivo del proyecto es diseñar una metodología para estimar el valor futuro de los niveles de glucosa obtenida a partir de un sensor subcutáneo, basada en la identificación recursiva del sistema glucorregulatorio a través de modelos lineales y determinando un horizonte de predicción óptimo de trabajo y analizando la influencia de la insulina en los resultados de la predicción. Se ha implementado un predictor paramétrico basado en un modelo autorregresivo ARX que predice con mejor precisión y con menor RMSE que un predictor ZOH a un horizonte de predicción de treinta minutos. Utilizar información relativa a la insulina no tiene efecto en la predicción. El preprocesado, postprocesado y el tratamiento de la estabilidad tienen un efecto muy beneficioso en la predicción. Diabetes mellitusis a group of metabolic diseases in which a person has high blood sugar, either because the body does not produce enough insulin, or because cells do not respond to the insulin produced. The insulin is a hormone that helps the glucose to reach to outlying tissues and the nervous system to supply energy. Nowadays, the actual technology allows raising the development of the “artificial endocrine pancreas”. It involves a continuous glucose sensor, an insulin bump, and a full closed loop algorithm that calculate the insulin units required by patient at any time, according to the glucose measure obtained by the sensor and any target. The main problem of the full closed loop systems is the delays, the glucose sensor measures the glucose in the interstitial fluid that represents the glucose was in the blood some time ago. Because of this, a change in the glucose in blood would take some time to be detected by the sensor. In addition, insulin units administered by a patient take about 20-30 minutes to reach the blood stream. In order to avoid this effect, it will try to predict the glucose level in the near future. To do that, a subcutaneous glucose predictor is used to predict the future glucose with the information about insulin and glucose. The goal of the proyect is to design a method in order to estimate the future valor of glucose obtained by a subcutaneous sensor. It is based on the recursive identification of the regulatory system through the linear models, determining optimal prediction horizon and analyzing the influence of insuline on the prediction results. A parametric predictor based in ARX autoregressive model predicts with better precision and with lesser RMSE than ZOH predictor in a thirty minutes prediction horizon. Using the relative insulin information has no effect in the prediction. The preprocessing, the postprocessing and the stability treatment have many advantages in the prediction.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Vigilancia por análisis de ruido de los sensores de temperatura

En este proyecto se van a aplicar las técnicas de análisis de ruido para caracterizar la respuesta dinámica de varios sensores de temperatura, tanto termorresistencias de platino como de termopares. Estos sensores son imprescindibles para él correcto funcionamiento de las centrales nucleares y requieren vigilancia para garantizar la exactitud de las medidas. Las técnicas de análisis de ruido son técnicas pasivas, es decir, no afectan a la operación de la planta y permiten realizar una vigilancia in situ de los sensores. Para el caso de los sensores de temperatura, dado que se pueden asimilar a sistemas de primer orden, el parámetro fundamental a vigilar es el tiempo de respuesta. Éste puede obtenerse para cada una de las sondas por medio de técnicas en el dominio de la frecuencia (análisis espectral) o por medio de técnicas en el dominio del tiempo (modelos autorregresivos). Además de la estimación del tiempo de respuesta, se realizará una caracterización estadística de las sondas. El objetivo es conocer el comportamiento de los sensores y vigilarlos de manera que se puedan diagnosticar las averías aunque éstas estén en una etapa incipiente. ABSTRACT In this project we use noise analysis technique to study the dynamic response of RTDs (Resistant temperature detectors) and thermocouples. These sensors are essential for the proper functioning of nuclear power plants and therefore need to be monitored to guarantee accurate measurements. The noise analysis techniques do not affect plant operation and allow in situ monitoring of the sensors. Temperature sensors are equivalent to first order systems. In these systems the main parameter to monitor is the response time which can be obtained by means of techniques in the frequency domain (spectral analysis) as well as time domain (autoregressive models). Besides response time estimation the project will also include a statistical study of the probes. The goal is to understand the behavior of the sensors and monitor them in order to detect any anomalies or malfunctions even if they occur in an early stage.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

In situ surveillance of capacitive pressure transmitter dynamic poles

In this work, a methodology is proposed to find the dynamic poles of a capacitive pressure transmitter in order to enhance and extend the online surveillance of this type of sensor based on the response time measurement by applying noise analysis techniques and the dynamic data system procedure. Several measurements taken from a pressurized water reactor have been analyzed. The methodology proposes an autoregressive fit whose order is determined by the sensor dynamic poles. Nevertheless, the signals that have been analyzed could not be filtered properly in order to remove the plant noise; thus, this was considered as an additional pair of complex conjugate poles. With this methodology we have come up with the numerical value of the sensor second real pole in spite of its low influence on the sensor dynamic response. This opens up a more accurate online sensor surveillance since the previous methods were achieved by considering one real pole only.

Application of noise analysis for sensor surveillance in Trillo NPP

It is a known fact that noise analysis is a suitable method for sensor performance surveillance. In particular, controlling the response time of a sensor is an efficient way to anticipate failures and to have the opportunity to prevent them. In this work the response times of several sensors of Trillo NPP are estimated by means of noise analysis. The procedure applied consists of modeling each sensor with autoregressive methods and getting the searched parameter by analyzing the response of the model when a ramp is simulated as the input signal. Core exit thermocouples and in core self-powered neutron detectors are the main sensors analyzed but other plant sensors are studied as well. Since several measurement campaigns have been carried out, it has been also possible to analyze the evolution of the estimated parameters during more than one fuel cycle. Some sensitivity studies for the sample frequency of the signals and its influence on the response time are also included. Calculations and analysis have been done in the frame of a collaboration agreement between Trillo NPP operator (CNAT) and the School of Mines of Madrid.

AR modelling and DDS for in situ surveillance of capacitive pressure transmitters

In this work, a methodology is proposed to find the dynamics poles of a capacitive pressure transmitter in order to enhance and extend the on line surveillance of this type of sensors based on the response time measurement by applying noise analysis techniques and the Dynamic Data System. Several measurements have been analyzed taken from a Pressurized Water Reactor. The methodology proposes an autoregressive fit whose order is determined by the sensor dynamics poles. Nevertheless, the signals that have been analyzed, could not be filtered properly in order to remove the plant noise, thus, this was considered as an additional pair of complex conjugate poles. With this methodology we have come up with the numerical value of the sensor second real pole in spite of its low influence on the sensor dynamic response. This opens up a more accurate on line sensor surveillance since the previous methods were achieved by considering one real pole only.

Bayesian model selection of structural explanatory models.

In this study we are proposing a Bayesian model selection methodology, where the best model from the list of candidate structural explanatory models is selected. The model structure is based on the Zellner's (1971)explanatory model with autoregressive errors. For the selection technique we are using a parsimonious model, where the model variables are transformed using Box and Cox (1964) class of transformations.

Bayesian model selection of structural explanatory models: Application to road accident data

Using the Bayesian approach as the model selection criteria, the main purpose in this study is to establish a practical road accident model that can provide a better interpretation and prediction performance. For this purpose we are using a structural explanatory model with autoregressive error term. The model estimation is carried out through Bayesian inference and the best model is selected based on the goodness of fit measures. To cross validate the model estimation further prediction analysis were done. As the road safety measures the number of fatal accidents in Spain, during 2000-2011 were employed. The results of the variable selection process show that the factors explaining fatal road accidents are mainly exposure, economic factors, and surveillance and legislative measures. The model selection shows that the impact of economic factors on fatal accidents during the period under study has been higher compared to surveillance and legislative measures.

Poisson dynamic models. An application to interurban accidents in Spain

In this study we apply count data models to four integer–valued time series related to accidentality in Spanish roads applying both the frequentist and Bayesian approaches. The time series are: number of fatalities, number of fatal accidents, number of killed or seriously injured (KSI) and number of accidents with KSI. The model structure is Poisson regression with first order autoregressive errors. The purpose of the paper is first to sort out the explanatory variables by relevance and second to carry out a prediction exercise for validation.