Methods for comprehensive structural dynamic analysis generally employ input-output modal analysis with a mathematical model of structural vibration using excitation and response data. Recently operational modal analysis methods using only vibration response data have been developed. In this thesis, both input-output and operational modal analysis, in the presence of significant unmeasured excitations, is considered. This situation arises when a test structure cannot be effectively isolated from ambient excitations or where the operating environment imposes dynamically-important boundary conditions. The limitations of existing deterministic frequency-domain methods are assessed. A novel time-domain estimation algorithm, based on the estimation of a discrete-time autoregressive moving average with exogenous excitation (ARMAX) model, is proposed. It includes a stochastic component to explicitly account for unmeasured excitations and measurement noise. A criterion, based on the sign of modal damping, is incorporated to distinguish vibration modes from spurious modes due to unmeasured excitations and measurement noise, and to identify the most complete set of modal parameters from a group of estimated models. Numerical tests demonstrate that the proposed algorithm effectively identifies vibration modes even with significant unmeasured random and periodic excitations. Random noise is superimposed on response measurements in all tests. Simulated systems with low modal damping, closely spaced modes and high modal damping are considered independently. The accuracy of estimated modal parameters is good except for degreesof- freedom with a low response level but this could be overcome by appropriate placement of excitation and response measurement points. These observations are reflected in experimental tests that include unmeasured periodic excitations over 200% the level of measured excitations, unmeasured random excitations at 90% the level of measured excitations, and the superposition of periodic and random unmeasured excitations. Results indicate advantages of the proposed algorithm over a deterministic frequency domain algorithm. Piezoceramic plates are used for structural excitation in one experimental case and the limitations of distributed excitation for broadband analysis are observed and characterised in terms of actuator geometry and modal deformation. The ARMAX algorithm is extended for use with response measurements exclusively. Numerical and experimental tests demonstrate its performance using time series data and correlation functions calculated from response measurements.

Univ., Diss.--Karlsruhe, 2004.

SANTOS, C. P. B. Ensaios sobre demanda por energia elétrica, produtividade e eficiência no setor agrícola no Brasil e na América do Sul. 2017. 87 f. Dissertação (Mestrado em Economia Rural) - Centro de Ciências Agrárias, Universidade Federal do Ceará, Fortaleza, 2017 / Submitted by Carlene Miranda (carlenematias@hotmail.com) on 2017-05-03T12:25:27Z No. of bitstreams: 1 2017_dis_cpbsantos.pdf: 1707811 bytes, checksum: 5c286b9ccba722659c0b1679803bcdb9 (MD5) / Approved for entry into archive by Margareth Mesquita (margaret@ufc.br) on 2017-05-05T13:21:20Z (GMT) No. of bitstreams: 1 2017_dis_cpbsantos.pdf: 1707811 bytes, checksum: 5c286b9ccba722659c0b1679803bcdb9 (MD5) / Made available in DSpace on 2017-05-05T13:21:20Z (GMT). No. of bitstreams: 1 2017_dis_cpbsantos.pdf: 1707811 bytes, checksum: 5c286b9ccba722659c0b1679803bcdb9 (MD5) Previous issue date: 2017 / This dissertation is composed of two studies that address the demand for electricity in rural areas and total factor productivity (TFP) with decomposition using the Malmquist index, where each chapter consists of an article. The first study analyzed the impacts of the expansionary policies and the water crisis in the reservoirs of the Brazilian hydroelectric dams in the demand for electricity in the rural environment of Brazil, through a historical analysis of the formation of the Brazilian energy matrix, the management of generation indicators Of electric energy and by the approach of the economic policies expansionist and its impact in the increase of the demand for electric power in the rural environment. Using the electric power demand function, with the econometric methodology of selection of Backward models, with variables lagged in 4 time periods and through the application of autoregressive moving average vectors with exogenous variables (ARMAX). It can be verified that the exogenous variables applied to the model, were relevant for the estimation. From the results obtained with the model used, it was estimated the demand for electric energy in Brazil from June 2014 to March 2016, remaining the values estimated within the range of significance of 5% in relation to the real values obtained in this period. The methodology was adequate to predict the monthly consumption of electricity in rural Brazil. The second chapter proposes to measure the effects of access to electricity on rural productivity in the countries of South America, with the application of the frontier function of stochastic production proposed by Battese and Coelli (1995), with a Cobb- Douglas with no technical progress, where it is observed that the electric energy is around the inefficiency with a rate of 17.30%, for the set of countries under study, and through the Malmquist index obtained the decomposition of the total factor productivity In two periods, with a complete analysis of the database 1990 to 2012 and part of this database with the period between 2000 and 2012, where in both cases the improvement of TFP was in most countries as a function of an increase Of the access to technology, among them the consumption and access to electric energy by the families residing in the rural environment of the countries of South America. / Esta dissertação é composta por dois estudos que abordam a demanda por energia elétrica no meio rural e a produtividade total dos fatores (PTF) com decomposição por meio do índice de Malmquist, sendo que cada capítulo é constituído por um artigo. O primeiro estudo analisou os impactos das políticas expansionistas e a crise hídrica nos reservatórios das hidrelétricas brasileiras na demanda por energia elétrica no meio rural do Brasil, através de uma análise histórica da formação da matriz energética brasileira, da gestão dos indicadores de geração de energia elétrica e pela abordagem das políticas econômicas expansionistas e seu impacto na elevação da demanda por energia elétrica no meio rural. Para isso foi utilizada a função de demanda por energia elétrica, com a metodologia econométrica de seleção de modelos Backward, com variáveis defasadas em 4 períodos de tempo e através da aplicação de vetores autorregressivos de média móvel com variáveis exógenas (ARMAX). Foi possível constatar que as variáveis exógenas aplicadas ao modelo, mostraram-se relevantes quanto à estimação. A partir dos resultados obtidos com o modelo utilizado estimou-se a demanda por energia elétrica no Brasil, de junho 2014 a março de 2016, permanecendo os valores estimados dentro do intervalo de significância de 5%, em relação aos valores reais obtidos neste período, mostrando-se a metodologia adequada para previsão do consumo mensal de energia elétrica no meio rural do Brasil. O segundo estudo mensurou os efeitos do acesso à energia elétrica sobre a produtividade no meio rural dos países da América do Sul, com aplicação da função de fronteira de produção estocástica proposto por Battese e Coelli (1995), com uma função Cobb-Douglas sem progresso técnico. Observou-se que a energia elétrica é um redor da ineficiência, com índice de 17,30% para o conjunto de países em estudo. Através do índice de Malmquist obteve-se a decomposição da produtividade total dos fatores em 2 períodos, com uma análise completa da base de dados 1990 a 2012 e parte desta base com o período compreendido entre 2000 a 2012. Em ambos os casos, a melhoria da PTF apresentou-se em maior parte dos países como função de um aumento do acesso à tecnologia, dentre esta, o consumo e acesso à energia elétrica pelas famílias residentes no meio rural dos países da América do Sul.

Time series model have been used in many areas of knowledge and have become a current necessity for companies to survive in a globalized and competitive market, as well as climatic factors that have always been a concern because of the different ways they interfere in human life. In this context, this work aims to present a comparison among the performances by the following models of time series: ARIMA, ARMAX and Exponential Smoothing, adjusted to air relative humidity (UR) and also to verify the volatility present in the series through non-linear models ARCH/GARCH, adjusted to residues of the ARIMA and ARMAX models. The data were collected from INMET from October, 1st to January, 22nd, 2014. In the comparison of the results and the selection of the best model, the criteria MAPE, EQM, MAD and SSE were used. The results showed that the model ARMAX(3,0), with the inclusion of exogenous variables produced better forecast results, compared to the other models SARMA(3,0)(1,1)12 and the Holt-Winters multiplicative. In the volatility study of the series via non-linear ARCH(1), adjusted to the quadrants of SARMA(3,0)(1,1)12 and ARMAX(3,0) residues, it was observed that the volatility does not tend to influence the future long-term observations. It was then concluded that the classes of models used and compared in this study, for data of a climatologic variable, showed a good performance and adjustment. We highlight the broad usage possibility in the techniques of temporal series when it is necessary to make forecasts and also to describe a temporal process, being able to be used as an efficient support tool in decision making. / Modelos de séries temporais vêm sendo empregados em diversas áreas do conhecimento e têm surgido como necessidade atual para empresas sobreviverem em um mercado globalizado e competitivo, bem como fatores climáticos sempre foram motivo de preocupação pelas diferentes formas que interferem na vida humana. Nesse contexto, o presente trabalho tem por objetivo apresentar uma comparação do desempenho das classes de modelos de séries temporais ARIMA, ARMAX e Alisamento Exponencial, ajustados a dados de umidade relativa do ar (UR) e verificar a volatilidade presente na série por meio de modelos não-lineares ARCH/GARCH ajustados aos resíduos dos modelos ARIMA e ARMAX. Os dados foram coletados junto ao INMET no período de 01 de outubro de 2001 a 22 de janeiro de 2014. Na comparação dos resultados e na seleção do melhor modelo foram utilizados os critérios MAPE, EQM, MAD e SSE. Os resultados mostraram que o modelo ARMAX(3,0) com a inclusão de variáveis exógenas produziu melhores resultados de previsão em relação aos seus concorrentes SARMA(3,0)(1,1)12 e o Holt-Winters multiplicativo. No estudo da volatilidade da série via modelo não-linear ARCH(1), ajustado aos quadrados dos resíduos dos modelos SARMA(3,0)(1,1)12 e ARMAX(3,0), observou-se que a volatilidade não tende a influenciar as observações futuras em longo prazo. Conclui-se que as classes de modelos utilizadas e comparadas neste estudo, para dados de uma variável climatológica, demonstraram bom desempenho e ajuste. Destaca-se a ampla possibilidade de utilização das técnicas de séries temporais quando se deseja fazer previsões e descrever um processo temporal, podendo ser utilizadas como ferramenta eficiente de apoio nas tomadas de decisão.

Made available in DSpace on 2016-08-17T14:53:03Z (GMT). No. of bitstreams: 1 Jorge Farid Amate.pdf: 1076998 bytes, checksum: 7c54d6bc2833288f1945368aa6976936 (MD5) Previous issue date: 2009-02-06 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / In industrial processes, where parameters estimation and standard recognition are desired, digital filters technology is used to do estimation. The digital filter is responsible for prediction and filtering process. Then, the filter behavior can be analyzed based on performance, gains and others variables linked to the specified model. However, to obtain trusty variables and data to estimate the process in question, a model that represents well the physics plant is needed. To do this, are applied techniques based on Systems Identification where we obtain the ARX, ARMAX, Output-Error and Box-Jenkins models of the electrolytic pot. Results, validation and their analysis, applied in standard recognition, using different structures are presented. / Em processos industriais, onde deseja-se a estimação de parâmetros e reconhecimento de padrões, utiliza-se da tecnologia de filtros digitais para tal fim. O filtro digital é responsável pelo processo de predição e filtragem. Assim, pode-se fazer uma análise do comportamento do filtro baseada no desempenho, ganhos e outras variáveis ligadas ao modelo especificado. Porém, para obtenção de variáveis e dados confiáveis para estimar-se o processo em questão, necessita-se de um modelo que represente bem a planta física. Para isto, são aplicadas técnicas baseadas em Identificação de Sistemas, onde são obtidos os modelos ARX, ARMAX, Output-Error e Box-Jenkins da cuba eletrolítica. São apresentados os resultados, validações dos modelos e análise dos mesmos, aplicados ao reconhecimento de padrões, utilizando-se diferentes estruturas.

Ce mémoire reprend la méthode DPFT proposée par D. Duband pour identifier à la fois la fonction de transfert pluie efficace débit et la série des pluies efficaces déduites des pluies brutes observées . Dans ce travail on trouvera à une formalisation théorique de la méthode et des nombreuses améliorations pratiques au niveau des algorithmes et de la mise en oeuvre. La version améliorée est appliquée à 2 bassins versants réels et à un jeu de données simulées. A fin de comparaison, on applique aux mêmes données un modèle du type ARMAX et on compare les résultats avec ceux de la DPFT. On en déduit les conditions d'utilisation respectives des 2 mêthodes.

Submitted by Adriano Duarte da Silva (adriano_duarte_silva@yahoo.com.br) on 2017-03-28T16:30:51Z No. of bitstreams: 1 Dissertacao_Adriano_2016_Completo.pdf: 4047217 bytes, checksum: c2877f83944b388d03457348b61d7d00 (MD5) / Approved for entry into archive by Leiliane Silva (leiliane.silva@fgv.br) on 2017-05-11T13:18:25Z (GMT) No. of bitstreams: 1 Dissertacao_Adriano_2016_Completo.pdf: 4047217 bytes, checksum: c2877f83944b388d03457348b61d7d00 (MD5) / Made available in DSpace on 2017-05-12T12:51:45Z (GMT). No. of bitstreams: 1 Dissertacao_Adriano_2016_Completo.pdf: 4047217 bytes, checksum: c2877f83944b388d03457348b61d7d00 (MD5) Previous issue date: 2016-07-15 / The Brazilian aviation went through moments of supply and demand growth in the last decade, which triggered investment and plan decisions aiming at improving the Brazilian airspace control system and airports infrastructure as well as the services offered by airline companies (for instance aviation fuel demand increase). The main objective of this work is to contribute to the creation of a predictive model of takeoffs and landings for the short and medium run. We use the Box-Jenkins model (ARMA), combined with covariates, to predict the amount of takeoffs and landings of commercial aviation in four Brazilian airports: Galeão Airport (Rio de Janeiro), Brasília Airport (Distrito Federal), Guarulhos Airport (São Paulo) and Recife Airport (Pernambuco). We find that the models fit well in the short run, but in the medium run in forecast events such as the recent Brazilian economic crises can damage the predictions. The data analyzed in this work is owned by The Management Center of Air Navegation (CGNA), which is a military unit subordinated to the Departamento de Controle do Espaço Aéreo (DECEA). Therefore, the predictability of the demand for air traffic will help in the allocation of resources for air traffic management. / A aviação brasileira viveu momentos de crescimentos da oferta e da demanda nos últimos 10 anos, o que gerou a necessidade de planejar e investir no aumento da infraestrutura do Sistema de Controle do Espaço Aéreo Brasileiro (SISCEAB), do parque aeroportuário e dos serviços envolvidos na oferta e na demanda da aviação e das companhias aéreas (exemplo a demanda por Querosene de Aviação – QAV). O principal objetivo deste trabalho é contribuir para a criação de um modelo preditivo do comportamento das operações de pousos e decolagens dos principais aeroportos brasileiros para o curto e médio prazo. Utilizaremos o modelo Box-Jenkins (ARMA), combinado com covariáveis, para prever a quantidade de operações de pousos e decolagens da aviação comercial em quatro aeroportos brasileiros: Aeroporto do Galeão/RJ, Aeroporto de Brasília/DF, Aeroporto de Guarulhos/SP e Aeroporto de Recife/PE. Verificamos que os modelos se ajustam bem no curto prazo, mas que no médio prazo poderão necessitar de mais dados que podem interferir na quantidade de pousos e decolagens de forma atípica, como, por exemplo, a redução da malha aérea devido à crise econômica brasileira dos últimos 2 anos. Os dados utilizados neste trabalho são pertencentes ao Centro de Gerenciamento da Navegação Aérea (CGNA) que é uma unidade militar subordinada ao Departamento de Controle do Espaço Aéreo Brasileiro (DECEA). Portanto, a previsibilidade da demanda de tráfego aéreo ajudará na alocação de recursos no gerenciamento de tráfego aéreo.

La necesidad de conocer con suficiente tiempo de antelación los caudales futuros en ríos donde se asientan grandes ciudades e industrias es común en todas partes del mundo. Existen diversas metodologías que permiten resolver este problema, cada una con sus pros y sus contras. El acople y la comparación entre varios modelos de predicción de diferentes características es fundamental a la hora de analizar la situación futura en un caso de alerta, donde es necesario tomar decisiones trascendentales. En esta tesis se ha realizado una intensa revisión bibliográfica sobre los modelos de predicción con Redes Neuronales Artificiales (RNA) para conocer el estado del arte de esta metodología y, a partir de ese punto, proponer y estudiar mejoras que puedan contribuir a su avance. Con la intención de darle significado físico a este tipo de modelos, se ha propuesto una metodología de modelo híbrido que permite identificar automáticamente el estado hidrológico de una cuenca determinada, para permitir modelar por separado cada estado mediante RNA simples. También se ha incorporado el concepto físico en la elección de las variables de entrada al modelo, proponiendo análisis geomorfológicos de la cuenca y de tiempos de respuesta que ayuden a identificar las variables más influyentes. Por otro lado, dada la necesidad de conocer la función de distribución de las predicciones para casos reales, donde es necesario tomar decisiones a partir de estos resultados, se ha propuesto una metodología para el cálculo de la incertidumbre de las predicciones, pudiendo ser aplicado para cualquier tipo de modelo sin importar su complejidad. Para conferir un uso práctico a estas ideas, se ha desarrollado una aplicación informática (ANN) capaz de realizar los cálculos necesarios para la construcción de un modelo de predicción con RNA. / Pujol Reig, L. (2009). Predicción de caudales en tiempo real en grandes cuencas utilizando redes neuronales artificiales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6422

The past 30 years have witnessed major developments in vibration based damage detection and identification, also collectively referred to as damage diagnosis. Moreover, the past 10 years have seen a rapid increase in the amount of research related to Structural Health Monitoring (SHM) as quantified by the significant escalation in papers published on this subject. Thus, the increased interest in this engineering field and its associated potential constitute the main motive for this thesis. The goal of the thesis is the development and introduction of novel advanced functional and sequential statistical time series methods for vibration based damage diagnosis and SHM. After the introduction of the first chapter, Chapter II provides an experimental assessment and comparison of vibration based statistical time series methods for Structural Health Monitoring (SHM) via their application on a lightweight aluminum truss structure and a laboratory scale aircraft skeleton structure. A concise overview of the main non-parametric and parametric methods is presented, including response-only and excitation-response schemes. Damage detection and identification are based on univariate (scalar) versions of the methods, while both scalar (univariate) and vector (multivariate) schemes are considered. The methods' effectiveness for both damage detection and identification is assessed via various test cases corresponding to different damage scenarios, multiple experiments and various sensor locations on the considered structures. The results of the chapter confirm the high potential and effectiveness of vibration based statistical time series methods for SHM. Chapter III investigates the identification of stochastic systems under multiple operating conditions via Vector-dependent Functionally Pooled (VFP) models. In many applications a system operates under a variety of operating conditions (for instance operating temperature, humidity, damage location, damage magnitude and so on) which affect its dynamics, with each condition kept constant for a single commission cycle. Typical examples include mechanical structures operating under different environmental conditions, aircrafts under different flight conditions (altitude, velocity etc.), structures under different structural health states (various damage locations and magnitudes). In this way, damage location and magnitude may be considered as parameters that affect the operating conditions and as a result the structural dynamics. This chapter's work is based on the novel Functional Pooling (FP) framework, which has been recently introduced by the Stochastic Mechanical Systems \& Automation (SMSA) group of the Mechanical Engineering and Aeronautics Department of University of Patras. The main characteristic of Functionally Pooled (FP) models is that their model parameters and innovations sequence depend functionally on the operating parameters, and are projected on appropriate functional subspaces spanned by mutually independent basis functions. Thus, the fourth chapter of the thesis addresses the problem of identifying a globally valid and parsimonious stochastic system model based on input-output data records obtained under a sample of operating conditions characterized by more than one parameters. Hence, models that include a vector characterization of the operating condition are postulated. The problem is tackled within the novel FP framework that postulates proper global discrete-time linear time series models of the ARX and ARMAX types, data pooling techniques, and statistical parameter estimation. Corresponding Vector-dependent Functionally Pooled (VFP) ARX and ARMAX models are postulated, and proper estimators of the Least Squares (LS), Maximum Likelihood (ML), and Prediction Error (PE) types are developed. Model structure estimation is achieved via customary criteria (Bayesian Information Criterion) and a novel Genetic Algorithm (GA) based procedure. The strong consistency of the VFP-ARX least squares and maximum likelihood estimators is established, while the effectiveness of the complete estimation and identification method is demonstrated via two Monte Carlo studies. Based on the postulated VFP parametrization a vibration based statistical time series method that is capable of effective damage detection, precise localization, and magnitude estimation within a unified stochastic framework is introduced in Chapter IV. The method constitutes an important generalization of the recently introduced Functional Model Based Method (FMBM) in that it allows, for the first time in the statistical time series methods context, for complete and precise damage localization on continuous structural topologies. More precisely, the proposed method can accurately localize damage anywhere on properly defined continuous topologies on the structure, instead of pre-defined specific locations. Estimator uncertainties are taken into account, and uncertainty ellipsoids are provided for the damage location and magnitude. To achieve its goal, the method is based on the extended class of Vector-dependent Functionally Pooled (VFP) models, which are characterized by parameters that depend on both damage magnitude and location, as well as on proper statistical estimation and decision making schemes. The method is validated and its effectiveness is experimentally assessed via its application to damage detection, precise localization, and magnitude estimation on a prototype GARTEUR-type laboratory scale aircraft skeleton structure. The damage scenarios considered consist of varying size small masses attached to various continuous topologies on the structure. The method is shown to achieve effective damage detection, precise localization, and magnitude estimation based on even a single pair of measured excitation-response signals. Chapter V presents the introduction and experimental assessment of a sequential statistical time series method for vibration based SHM capable of achieving effective, robust and early damage detection, identification and quantification under uncertainties. The method is based on a combination of binary and multihypothesis versions of the statistically optimal Sequential Probability Ratio Test (SPRT), which employs the residual sequences obtained through a stochastic time series model of the healthy structure. In this work the full list of properties and capabilities of the SPRT are for the first time presented and explored in the context of vibration based damage detection, identification and quantification. The method is shown to achieve effective and robust damage detection, identification and quantification based on predetermined statistical hypothesis sampling plans, which are both analytically and experimentally compared and assessed. The method's performance is determined a priori via the use of the analytical expressions of the Operating Characteristic (OC) and Average Sample Number (ASN) functions in combination with baseline data records, while it requires on average a minimum number of samples in order to reach a decision compared to most powerful Fixed Sample Size (FSS) tests. The effectiveness of the proposed method is validated and experimentally assessed via its application on a lightweight aluminum truss structure, while the obtained results for three distinct vibration measurement positions prove the method's ability to operate based even on a single pair of measured excitation-response signals. Finally, Chapter VI contains the concluding remarks and future perspectives of the thesis. / Κατά τη διάρκεια των τελευταίων 30 ετών έχει σημειωθεί σημαντική ανάπτυξη στο πεδίο της ανίχνευσης και αναγνώρισης βλαβών, το οποίο αναφέρεται συνολικά και σαν διάγνωση βλαβών. Επίσης, κατά την τελευταία δεκαετία έχει σημειωθεί σημαντική πρόοδος στον τομέα της παρακολούθησης της υγείας (δομικής ακεραιότητας) κατασκευών. Στόχος αυτής της διατριβής είναι η ανάπτυξη εξελιγμένων συναρτησιακών και επαναληπτικών μεθόδων χρονοσειρών για τη διάγνωση βλαβών και την παρακολούθηση της υγείας κατασκευών υπό ταλάντωση. Αρχικά γίνεται η πειραματική αποτίμηση και κριτική σύγκριση των σημαντικότερων στατιστικών μεθόδων χρονοσειρών επί τη βάσει της εφαρμογής τους σε πρότυπες εργαστηριακές κατασκευές. Εφαρμόζονται μη-παραμετρικές και παραμετρικές μέθοδοι που βασίζονται σε ταλαντωτικά σήματα διέγερσης και απόκρισης των κατασκευών. Στη συνέχεια αναπτύσσονται στοχαστικά συναρτησιακά μοντέλα για την στοχαστική αναγνώριση κατασκευών υπό πολλαπλές συνθήκες λειτουργίας. Τα μοντέλα αυτά χρησιμοποιούνται για την αναπαράσταση κατασκευών σε διάφορες καταστάσεις βλάβης (θέση και μέγεθος βλάβης), ώστε να είναι δυνατή η συνολική μοντελοποίσή τους για όλες τις συνθήκες λειτουργίας. Τα μοντέλα αυτά αποτελούν τη βάση στην οποία αναπτύσσεται μια συναρτησιακή μέθοδος η οποία είναι ικανή να αντιμετωπίσει συνολικά και ενιαία το πρόβλημα της ανίχνευσης, εντοπισμού και εκτίμησης βλαβών σε κατασκευές. Η πειραματική αποτίμηση της μεθόδου γίνεται με πολλαπλά πειράματα σε εργαστηριακό σκελετό αεροσκάφους. Στο τελευταίο κεφάλαιο της διατριβής προτείνεται μια καινοτόμος στατιστική επαναληπτική μέθοδο για την παρακολούθηση της υγείας κατασκευών. Η μέθοδος κρίνεται αποτελεσματική υπό καθεστώς λειτουργικών αβεβαιοτήτων, καθώς χρησιμοποιεί επαναληπτικά και στατιστικά τεστ πολλαπλών υποθέσεων. Η αποτίμηση της μεθόδου γίνεται με πολλαπλά εργαστηριακά πειράματα, ενώ η μέθοδος κρίνεται ικανή να λειτουργήσει με τη χρήση ενός ζεύγους ταλαντωτικών σημάτων διέγερσης-απόκρισης.

In a deregulated electricity market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. The forecasting of electricity market clearing price (MCP) is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. The mid-term electricity MCP forecasting focuses electricity MCP on a time frame from one month to six months. Developing mid-term electricity MCP forecasting is essential for mid-term planning and decision making, such as generation plant expansion and maintenance schedule, reallocation of resources, bilateral contracts and hedging strategies. Six mid-term electricity MCP forecasting models are proposed and compared in this thesis: 1) a single support vector machine (SVM) forecasting model, 2) a single least squares support vector machine (LSSVM) forecasting model, 3) a hybrid SVM and auto-regression moving average with external input (ARMAX) forecasting model, 4) a hybrid LSSVM and ARMAX forecasting model, 5) a multiple SVM forecasting model and 6) a multiple LSSVM forecasting model. PJM interconnection data are used to test the proposed models. Cross-validation technique was used to optimize the control parameters and the selection of training data of the six proposed mid-term electricity MCP forecasting models. Three evaluation techniques, mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square root error (MSRE), are used to analysis the system forecasting accuracy. According to the experimental results, the multiple SVM forecasting model worked the best among all six proposed forecasting models. The proposed multiple SVM based mid-term electricity MCP forecasting model contains a data classification module and a price forecasting module. The data classification module will first pre-process the input data into corresponding price zones and then the forecasting module will forecast the electricity price in four parallel designed SVMs. This proposed model can best improve the forecasting accuracy on both peak prices and overall system compared with other 5 forecasting models proposed in this thesis.