In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Trend forecasting is perceived to be an essential service for fashion companies to use in order to stay competitive in the fast-paced fashion industry. Yet, in times of climate change, appointing new trends each season is a questioned practice. Since trend forecasting aligns with the inherent obsolescence of fashion’s constant change, forecasting seems to stand in paradox with the imperatives of sustainability. Thus, this thesis aims to explore the role of trend forecasting to understand its compatibility with environmental sustainability. The review of previous research depicts the evolution of the trend forecasting field and displays prominent literature within fashion and sustainability, which together displays an apparent research gap that this study aims to fill. The thesis follows an exploratory design pursuing a multiple case study strategy applied through eight semi-structured interviews with trend forecasters and a content analysis of WGSN online trend forecasts. Findings of this study validate the existence of a trend forecasting paradox while also demonstrating areas of compatibilities. Customized forecasting and long-term approaches were concluded as compatible practices and may be integrated into both long-term and seasonal forecasting. This study also recognizes a need to differ between forecasting sustainability and sustainable forecasting. This thesis is believed contribute to an under-researched area and aid the trend forecasting industry to realize its impact on sustainability, as well as suggesting approaches on how to further incorporate sustainable practices into their work.

Uma vez que sistemas de inferência neuro-fuzzy adaptativos são aproximadores universais que podem ser usados em aplicações de aproximação de funções e de previsão, este trabalho tem por objetivo determinar seus melhores parâmetros e suas melhores arquiteturas com o propósito de se executar previsão de demanda de energia elétrica no curtíssimo prazo em subestações de distribuição. Isto pode possibilitar o desenvolvimento de controles automáticos de carga mais eficientes para sistemas elétricos de potência. As entradas do sistema são séries temporais de demanda de energia elétrica, compostas por dados mensurados em intervalos de cinco minutos ao longo de sete dias em subestações localizadas em cidades do interior do estado de São Paulo. Diversas configurações de entrada e diferentes arquiteturas foram examinadas para se fazer a previsão de um passo a frente. Os resultados do sistema de inferência neuro-fuzzy adaptativo frente às abordagens encontradas na literatura foram promissores. / Since adaptive neuro-fuzzy inference systems are universal approximators that can be used in functions approximation and forecasting applications, this work has the objective to determine their best parameters and best architectures with the purpose to execute very short term load forecasting in distribution substations. This can allow the development of more efficient load automatic control for power systems. The system inputs are load demand time series, which are composed of data measured at each five minutes interval, during seven days, from substations located in cities from São Paulo state countryside. Several input configurations and different architectures were examined to make a prediction aiming one step forecasting. The adaptive neuro-fuzzy inference system results in comparison with other approaches found in literature were promising.

This thesis aims to produce short-term forecasts for the economic activity in Turkey. As a proxy for the economic activity, industrial production index is used. Univariate autoregressive distributed lag (ADL) models, vector autoregressive (VAR) models and combination forecasts method are utilized in a pseudo out-of-sample forecasting framework to obtain one-month ahead forecasts. To evaluate the models&rsquo / forecasting performances, the relative root mean square forecast error (RRMSFE) is calculated. Overall, results indicate that combining the VAR models with four endogenous variables yields the most substantial improvement in forecasting performance, relative to benchmark autoregressive (AR) model.

Uma vez que sistemas de inferência neuro-fuzzy adaptativos são aproximadores universais que podem ser usados em aplicações de aproximação de funções e de previsão, este trabalho tem por objetivo determinar seus melhores parâmetros e suas melhores arquiteturas com o propósito de se executar previsão de demanda de energia elétrica no curtíssimo prazo em subestações de distribuição. Isto pode possibilitar o desenvolvimento de controles automáticos de carga mais eficientes para sistemas elétricos de potência. As entradas do sistema são séries temporais de demanda de energia elétrica, compostas por dados mensurados em intervalos de cinco minutos ao longo de sete dias em subestações localizadas em cidades do interior do estado de São Paulo. Diversas configurações de entrada e diferentes arquiteturas foram examinadas para se fazer a previsão de um passo a frente. Os resultados do sistema de inferência neuro-fuzzy adaptativo frente às abordagens encontradas na literatura foram promissores. / Since adaptive neuro-fuzzy inference systems are universal approximators that can be used in functions approximation and forecasting applications, this work has the objective to determine their best parameters and best architectures with the purpose to execute very short term load forecasting in distribution substations. This can allow the development of more efficient load automatic control for power systems. The system inputs are load demand time series, which are composed of data measured at each five minutes interval, during seven days, from substations located in cities from São Paulo state countryside. Several input configurations and different architectures were examined to make a prediction aiming one step forecasting. The adaptive neuro-fuzzy inference system results in comparison with other approaches found in literature were promising.

abstract: As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an existing wind farm are studied to characterize persistence forecasting errors using extreme value analysis techniques. In addition, a novel metric that quantifies the amount of non-stationarity in time series wind power data was proposed and used in a real-time algorithm to provide a rigorous method that adaptively determines training data for forecasts. Lastly, large swings in generation or load can cause system frequency and tie-line flows to deviate from nominal, so an anticipatory MPC-based secondary control scheme was designed and integrated into an automatic generation control loop to improve the ability of an interconnection to respond to anticipated large events and fluctuations in the power system. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

[pt] Quando ocorre algum tipo de falta em uma Linha de Transmissão (LT), sua localização exata é essencial para uma rápida recomposição do Sistema Elétrico. Métodos que utilizam tensão e corrente de apenas um terminal contêm simplificações que podem acarretar erros. Esta dissertação investiga a aplicação de Redes Neurais Artificiais (RNA) na obtenção de uma nova forma de identificar o tipo do curto- circuito e determinar a sua localização, utilizando dados obtidos em somente um terminal. O trabalho consiste de 4 partes principais: estudo bibliográfico da área de Redes Neurais; simulações de faltas para a obtenção de padrões; definição e implementação dos modelos de Redes Neurais para identificação e localização da falta; e estudos de casos. Na fase do estudo bibliográfico sobre RNA, foi verificado que as topologias de redes mais usuais são as Feed Forward, que podem ter uma ou mais camadas de Elementos Processadores (EP), sendo as redes com múltiplas camadas (Multi-Layer) a configuração mais completa. Para treinamento da rede, o algoritmo de aprendizado mais utilizado é o Back Propagation. Como fruto deste estudo bibliográfico é apresentado neste trabalho um resumo sobre RNA. Nas simulações de faltas para obtenção dos padrões de treinamento e teste, foi utilizado um sistema automático que, através da combinação de vários parâmetros do sistema elétrico, gera situações diferentes de falta. Este sistema utiliza como base o programa Alternative Transient Program -ATP. Neste trabalho o sistema elétrico está representado por uma LT de 345 KV, com fontes equivalentes representando um sistema real de Furnas Centrais Elétricas. Todos o sinais de tensão e corrente utilizados são representados por fasores de 60 Hz, obtidos através da Transformada Discreta de Fourier (TDF). Os modelos de RNAs para identificação e localização de falta foram implementados com sub-rotinas de redes neurais do programa MATLAB ver. 6.0, representados por Redes Perceptron Multicamadas (Multi Layer Perceptron), treinadas com algoritmo Back Propagation com taxa de aprendizado adaptativa e o termo momentum fixo. Um modelo único de RNA identifica quais as fases (A, B, C e T) envolvidas, classificando o tipo de falta, que pode ser: Monofásica; Bifásica; Bifásica-Terra ou Trifásica. Para a localização da falta, foram definidas 4 arquiteturas de RNA, uma para cada tipo de falta. A ativação de cada topologia de RNA para localização é definida em função do tipo de falta classificada no modelo de identificação com RNA. Na etapa de estudo de casos testou-se o desempenho de cada modelo de RNA utilizando casos de testes em outras situações de falta, diferentes dos conjuntos de treinamento. A RNA de identificação de falta foi avaliada para situações de faltas envolvendo outras LTs, com diferentes níveis de tensão. Os resultados das 4 RNAs de localização da falta foram comparados com os resultados obtidos utilizando o método tradicional, tanto para os casos simulados quanto para algumas situações reais de falta. A utilização de RNAs para a identificação e a localização de falta mostrouse bastante eficiente para os casos analisados, comprovando a aplicabilidade das redes neurais nesse problema. / [en] When a kind of fault occurs in a Transmission Line, its exact location is essential for a fast reclosing of the Electric System. Methods that use voltages and currents from only one terminal contain simplifications that can to cause mistakes. This paper presents an investigation about application of Artificial Neural Network (ANN) obtaining a new way of identification for the type of the short circuit and its location, using data obtained only in one terminal. The work consists on the following 4 main parts: bibliographical study of Neural Network`s area; simulations of faults in order to obtain of patterns; definition and implementation of Neural Network`s models for identification and location of the fault; and studies of cases. In the bibliographical study step on ANN, it was verified that the topologies for the more usual nets are Feed-

Dans ces travaux de thèse, nous proposons un modèle de prévision à court terme des flux de voyageurs basé sur les réseaux bayésiens. Ce modèle est destiné à répondre à des besoins opérationnels divers liés à l'information voyageurs, la régulation des flux ou encore la planification de l'offre de transport. Conçu pour s'adapter à tout type de configuration spatiale, il permet de combiner des sources de données hétérogènes (validations des titres de transport, comptages à bord des trains et offre de transport) et fournit une représentation intuitive des relations de causalité spatio-temporelles entre les flux. Sa capacité à gérer les données manquantes lui permet de réaliser des prédictions en temps réel même en cas de défaillances techniques ou d'absences de systèmes de collecte / In this thesis, we propose a Bayesian network model for short-term passenger flow forecasting. This model is intended to cater for various operational needs related to passenger information, passenger flow regulation or operation planning. As well as adapting to any spatial configuration, it is designed to combine heterogeneous data sources (ticket validation, on-board counts and transport service) and provides an intuitive representation of the causal spatio-temporal relationships between flows. Its ability to deal with missing data allows to make real-time predictions even in case of technical failures or absences of collection systems

The aim of this dissertation is to develop an ensemble algorithm for short-term electricity consumption forecasting and to assess the impact of meteorological factors and other features on the quality of the model. The work addresses issues related to improving the accuracy of electricity consumption forecasting using open meteorological data through ensemble machine learning methods and hyperparameter tuning algorithms. A review and analysis of existing methods for time series forecasting are conducted, taking into account the specific characteristics of electricity consumption time series forecasting. Additionally, a machine learning algorithm is developed using various factors as features for model training. The developed algorithm is tested on electricity consumption data from the Siberian Regional Dispatch Office. / Целью диссертационной работы является разработка ансамблевого алгоритма для краткосрочного прогнозирования электропотребления и оценка влияния на качество модели метеофакторов н других признаков. В работе рассматриваются вопросы повышения точности прогнозирования электропотребления с использованием открытых метеорологических данных с применением ансамблевых методов машинного обучения, и алгоритма подбора гиперпараметров моделей. В работе проведен обзор и анализ существующих методов для прогнозирования временных рядов с учетом особенностей прогнозирования временного ряда электропотребления. Также в работе разработан алгоритм машинного обучения с использованием различных факторов в качестве признаков для обучения моделей. Разработанный алгоритм протестирован на данных электропотребления в зоне ответственности ОДУ Сибири.

This work addresses the issues of improving the accuracy of forecasting the generation of photovoltaic power plants based on open meteorological data using machine learning methods and a preprocessing algorithm for the initial data. / В работе рассматриваются вопросы повышения точности прогнозирования генерации фотоэлектрических станций на открытых метеорологических данных с использованием методов машинного обучения, и алгоритма предварительной обработки исходных данных.