Buildings and the built environment account for a significant portion of the global energy use and greenhouse gas emissions, and reducing the energy demand in this sector is crucial for a sustainable energy transition. This highlights the need for accurate and large-scale estimations and predictions of the future energy demand in buildings. Urban building energy modeling (UBEM) is an analytical tool for precise and high-quality energy modelling of city-scale building stocks, which is growing in interest as a useful tool for researchers and decision-makers worldwide. This thesis contributes to the understanding and future development in the field of UBEM and multi-variate cluster analysis. Based on a review of contemporary literature, possible improvements and knowledge gaps regarding UBEM are identified. The majority of UBEM studies are developed for similar applications, and some challenges are close to universal. Difficulties in data acquisition and the identification and characterisation of building archetypes are frequently addressed. Drawing on conclusions from the review, a clustering methodology for identifying building archetypes for hybrid UBEM was developed. The methodology utilised the k-means cluster analysis algorithm for multiple diverse parameters, including socio-economic indicators, and is based on open data sets which eliminates data acquisition issues and allows for easy adaptation. Building archetypes were successfully identified for two large data sets, and proved to be representative of the sample building stock. The results of the analysis also show that the error metric values diverge after a certain number of clusters, for multiple runs of the algorithm. This property of the algorithm in combination with the use of both existing and novel error metrics provide a reliable method for determining the optimal number of clusters. The methodology developed in this thesis enables for an improved modelling process, as a part of a complete UBEM.

The majority of manufacturers provide their customers with volume discounts for placing repeat purchases or placing larger orders. In today's highly competitive market, the topic of how precisely a big number of products should be grouped together naturally emerges.\\In this context, three research questions that were directly relevant to the setting were formulated and their answers were provided. In order to achieve this goal, a number of experiments were carried out. In this particular instance, an algorithm was developed that determines the order combination that is mathematically superior to all others. In this context, an annual order cost saving of 1.33\% could be achieved based on the orders from the year 2021. This could be accomplished without the utilisation of heuristics for a limited number of products at most. In addition, a number of other heuristics have been devised for higher order combination sets. In addition, two other approaches to demand forecasting were investigated, and it was discovered that the time series in this particular instance was insufficient for the application of an RNN-LSTM model.

Every new product launching industrial company faces the difficulties of forecasting future success or failure of a new product before launch. Before launch it is common to develop a business case in order to estimate future quantities and set prices. In the present paper the challenges of developing a standardized business case tool for a large industrial construction and mining company are presented. Few academic studies have been conducted on the challenges and complexities of developing business cases. The research question under which this study is done is: What are the challenges associated with developing an effective standardized business case tool for a large industrial construction and mining company? Due to the different subject areas of the business case for new product launch, the challenges are categorized by topics developed by the researcher in the course of this project: process and team, data gathering and validation, quantity forecast and price forecast. The main challenges found in these categories by the researcher are: finding and motivating experts for the project of developing a standardized business case, gathering and selecting all data necessary without including redundant data, ensuring that different potential new products can be forecasted and designing the price forecast to be profit-maximizing. Solutions to these challenges are provided in the context of a case company by using methods suggested by the academic literature and the evaluation of expert interviews inside the case company

Demand forecasting plays an important role for every business and gives companies an opportunity to prepare for coming shifts in the market. The empirical findings of this study aim to support construction equipment manufacturers, distributors, and suppliers in apprehending the equipment market in more depth and foreseeing market demand to be able to adjust their business strategies and production capacities, allocate resources more efficiently, optimize the level of output and stock and, as a result, reduce associated costs, increase profitability and competitiveness. It is demonstrated that demand for construction equipment is heavily influenced by changes in economic conditions and country-specific economic indicators can serve as reliable input parameters to anticipate fluctuations in the construction equipment market. The Artificial Neural Networks (ANN) forecasting technique has been successfully employed to predict sales of construction equipment four quarters ahead in selected countries (Germany, The United Kingdom, France, Italy, Norway, Russia, Turkey and Saudi Arabia) with country related economic indicators used as an input.

Demand forecasting for sales is a widely researched topic that is essential for a business to prepare for market changes and increase profits. Existing research primarily focus on data that is more suitable for machine learning applications compared to the data accessible to companies lacking prior machine learning experience. This thesis performs demand forecasting on a known sales dataset and a dataset accessed directly from such a company, in the hopes of gaining insights that can help similar companies better utilize machine learning in their business model. LigthGBM, Linear Regression and Random Forest models are used along with several regression error metrics and plots to compare the performance of the two datasets. Both data sets are preprocessed into the same structure based on equivalent features found in each set. The company dataset is determined to be unfit for machine learning forecasting even after preprocessing measures and multiple possible reasons are established. The main contributors are a lack of observations per article and uniformity through the time series.

This thesis is focused on two topics. The first topic is the General Aviation (GA) demand forecasting models. The contributions to this topic are three fold: 1) we calibrated an econometric model to investigate the impact of fuel price on the utilization rate of GA piston engine aircraft, 2) we adopted a logistic model to identify the relationship between fuel price and an aircraft's probability of staying active, and 3) we developed an econometric model to forecast the airport-level itinerant and local GA operations. Our calibration results are compared with those reported in literature. Demand forecasts are made with these models and compared with those prepared by the Federal Aviation Administration. The second topic is to model the air traffic in the Organized Track System (OTS) over the North Atlantic. We developed a discrete-time event model to simulate the air traffic that uses the OTS. We proposed four new operational procedures to improve the flight operations for the OTS. Two procedures aim to improve the OTS assignments in the OTS entry area, and the other two aim to benefit flights once they are inside the OTS. The four procedures are implemented with the simulation model and their benefits are analyzed. Several implementation issues are discussed and recommendations are given. / Ph. D.

Electric grids across the world are experiencing an ever increasing number of extreme events ranging from extreme weather events to cyberattacks. Such extreme events have the potential to cause widespread power outages and even a blackout. A vast majority of power outages impacting the U.S. electric grid impact the distribution system. There are an estimated five million miles of distribution lines in the US electric grid. A majority of these lines are low-clearance overhead lines making them even more susceptible to damage during extreme events. However, this vital component of the U.S. electric grid remained neglected until recently. In recent decades, the integration of distributed energy resources (DERs) such as solar photovoltaic systems and battery energy storage systems at the grid edge have provided a major opportunity for enhancing the resilience of distribution systems. These DERs can be used to restore power supply when the bulk grid becomes unavailable. However, managing the interactions among different types of DERs has been challenging. Low inertia and significant differences in time constants of operation between conventional generation and inverter based resources (IBRs) are some of these challenges. Widespread deployment of microgrid controller capabilities can be a promising solution to manage these interactions. However, due to interoperability and integration challenges of optimization and dynamics control systems, power conversion systems and communication systems, the adoption of microgrids especially in underserved communities has been slow. The research presented in this dissertation is a significant step forward in this direction by proposing an approach which integrates optimal dispatch, sequential microgrid restoration and control algorithms. Potential cyberattack paths are identified by creating a detailed cyber-physical system model for microgrids. A two-tiered intrusion detection system is developed to detect and mitigate cyberattacks within the cyber layer itself. The developed sequential microgrid restoration algorithm coordinates optimal DER dispatch with the operation of legacy devices with no remote control or communication capabilities and net-metered loads with limited communications. By better utilizing the control capabilities of IBRs, reliance on low-latency centralized control algorithms has also been reduced. The developed approach systematically ensures adequate availability of control during dispatch and restoration to maintain microgrid stability. This research can thus pave the way for faster and more cost-effective deployment of microgrids. / Doctor of Philosophy / A U.S. National Academy of Engineering report has described the power grid as the greatest engineering achievement of the 20th century. The power grid is a complex interconnected system consisting of the power transmission system and the distribution system. The power transmission system consists of the power lines seen while driving on the freeways and the large power generating stations consisting of renewable, coal or nuclear power plants. Ensuring the reliable operation of the transmission system has always been a priority. The distribution system on the other hand consists of pole top transformers seen closer to homes which reduce the voltage to levels safe for electrical appliances. It also consists of the millions of miles of low-clearance overhead distribution lines deployed across the U.S. that provide electricity to every household. This critical part of U.S. electricity infrastructure had remained neglected which is the reason why 90% of power outages impact the distribution system. In recent decades, the integration of renewable energy sources like solar systems and battery storage systems has created an unprecedented opportunity for increasing the resilience of distribution systems against extreme events. These energy sources can provide power supply when the transmission system becomes unavailable. However, ensuring safe and reliable integrated operation of these sources with conventional diesel generators especially while isolated from the transmission system is challenging. This is where microgrids, which are self-sufficient miniature power grids, can help. Microgrids provide required control, communication and cybersecurity features necessary for reliable integrated operation of renewable and conventional energy sources. However, the challenges involved with interoperability of these systems has slowed down the deployment of microgrids especially in underserved communities. This is the research gap addressed in this dissertation. This research provides an approach for integrating the optimization, control, power electronics and cybersecurity systems. Reliance on expensive low-latency communication systems is reduced by utilizing the emerging capabilities of power electronics devices used for integrating the renewable energy sources with the electric power grid. Voltage control devices already deployed in the distribution systems which do not have remote control or communication capabilities have also been coordinated with energy sources. The research presented in this dissertation is a significant step forward for increasing access to power supply during outages and for reducing the time and cost of deployment of microgrids.

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The road traffic along with other key infrastructure sectors such as telecommunication, power, etc. has an important role in economic and technological growth of one country. Traffic engineers and analysts are responsible for solving a diversity of traffic problems, such as traffic data acquisition and evaluation. In response to the need to improve traffic operation, researchers implement advanced technologies and integration of systems and data, and develop state-of-the-art applications. This thesis introduces three novel web applications with an aim to offer traffic operators, managers, and analysts’ possibility to monitor the congestion, and analyze incidents and signal performance measures. They offer more detailed analysis providing users with insights from different levels and perspectives. The benefit of providing these visualization tools is more efficient estimation of the performance of local networks, thus facilitating the decision making process in case of emergency events. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

nÃo hà / The application of time series in varius areas such as engineering, logistics, operations research and economics, aims to provide the knowledge of the dependency between observations, trends, seasonality and forecasts. Considering the lack of effective supporting methods od logistics planning in the area of foreign trade, the multivariate models habe been presented and used in this work, in the area of time series: vector autoregression (VAR), vector autoregression moving-average (VARMA) and state-space integral equation (SS). These models were used for the analysis of demand forecast, the the bivariate series of value and volume of cashew nut exports from Cearà from 1996 to 2012. The results showed that the model state space was more successful in predicting the variables value and volume over the period that goes from january to march 2013, when compared to other models by the method of root mean squared error, getting the lowest values for those criteria. / A aplicaÃÃo de sÃries temporais em diversas Ãreas como engenharia, logÃstica, pesquisa operacional e economia, tem como objetivo o conhecimento da dependÃncia entre dados, suas possÃveis tendÃncias, sazonalidades e a previsÃo de dados futuros. Considerando a carÃncia de mÃtodos eficazes de suporte ao planejamento logÃstico na Ãrea de comÃrcio exterior, neste trabalho foram apresentados e utilizados os modelos multivariados, na Ãrea de sÃries temporais: auto-regressivo vetorial (VAR), auto-regressivomÃdias mÃveis vetorial (ARMAV) e espaÃo de estados (EES). Estes modelos foram empregados para a anÃlise de previsÃo de demanda, da sÃrie bivaria de valor e volume das exportaÃÃes cearenses de castanha de caju no perÃodo de 1996 à 2012. Os resultados mostraram que o modelo espaÃo de estados foi mais eficiente na previsÃo das variÃveis valor e volume ao longo do perÃodo janeiro à marÃo de 2013, quando comparado aos demais modelos pelo mÃtodo da raiz quadrada do erro mÃdio quadrÃtico, obtendo os menores valores para o referido critÃrio.