The historical underinvestment in power infrastructure and the poor performance of power delivery has resulted in extensive and regular power shortages in Nigeria. As Nigeria aims to bridge its power supply gap, the recent deregulation of its electricity market has seen the privatisation of its generation and distribution companies. Ambitious plans have also been put in place to expand the transmission network and the total power generation capacity. However, these plans have been developed with essentially arbitrary estimates for prevailing demand levels as the network and generation limits mean actual demand cannot be measured directly due to a programme of almost constant load shedding; the managed and intermittent distribution of inadequate energy allocation from the system operator. Network expansion planning and system reliability analysis require time series demand data to assess generation adequacy and to evaluate the impact of daily and seasonal influences on the energy supply-demand balance. To facilitate such analysis this thesis describes efforts to develop a credible time series electricity demand model for Nigeria. The focus of the approach has been to develop a fundamental bottom-up model of individual households accounting for a range of dwelling characteristics, socioeconomic factors, appliance use and household activities. A householder survey was conducted to provide essential inputs to allow a portfolio of household demand models which can account for weather-dependence and other factors. A range of national and regional socioeconomic and weather datasets have been employed to create a regionally disaggregated time series demand model. The generated demand estimates are validated against metered data obtained from Nigeria. The value of the approach is highlighted by using the model to investigate the potential for future load growth as well as analyse the impact of renewable energy generation on the Nigerian grid.

Demand response (DR) is one of the integral mechanisms of today's smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector.

This study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence, developing the analytics on optimal power-distribution across pertinent power-grids are verified with the models proposed. Forecast algorithms and computational outcomes on supply-demand performance are indicated and illustratively explained using real-world data sets. This study on electric utility takes duly into considerations of both deterministic (technological factors) as well as stochastic variables associated with the available resource-capacity and demand-profile details. Thus, towards forecasting exercise as above, a representative load-curve (RLC) is defined; and, it is optimally determined using an Artificial Neural Network (ANN) method using the data availed on supply-demand characteristics of a practical power-grid. This RLC is subsequently considered as an input parametric profile on tariff policies associated with electric power product-cost. This research further focuses on developing an optimal/suboptimal electric-power distribution scheme across power-grids deployed between multiple resources and different sets of user demands. Again, the optimal/suboptimal decisions are enabled using ANN-based simulations performed on load sharing details. The underlying supply-demand forecasting on distribution service profile is essential to support predictive designs on the amount of power required (or to be generated from single and/or multiple resources) versus distributable shares to different consumers demanding distinct loads. Another topic addressed refers to a business model on a cost reflective tariff levied in an electric power service in terms of the associated hedonic heuristics of customers versus service products offered by the utility operators. This model is based on hedonic considerations and technoeconomic heuristics of incumbent systems In the ANN simulations as above, bootstrapping technique is adopted to generate pseudo-replicates of the available data set and they are used to train the ANN net towards convergence. A traditional, multilayer ANN architecture (implemented with feed-forward and backpropagation techniques) is designed and modified to support a fast convergence algorithm, used for forecasting and in load-sharing computations. Underlying simulations are carried out using case-study details on electric utility gathered from the literature. In all, ANN-based prediction of a representative load-curve to assess power-consumption and tariff details in electrical power systems supporting a smart-grid, analysis of load-sharing and distribution of electric power on smart grids using an ANN and evaluation of electric power system infrastructure in terms of tariff worthiness deduced via hedonic heuristics, constitute the major thematic efforts addressed in this research study. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Hops are one of the four main ingredients used to produce beer. Many studies have been done to analyze the science behind growing and harvesting hops, creating hop hybrids, and how to brew beer with hops. However, there has been little research done revolving around the economic demand and supply model of the hop market. The objectives of this study are to create an econometric model of supply and demand of hops in the United States from 1981 to 2012, and to identify important exogenous variables that explain the supply and demand of hops using the two-stage least squares (2SLS) method of analysis. Using the 2SLS method, the demand model yielded that the US beer production variable is significant at the 10 percent level. For every 1 percent change in US beer production, there will be a 6.25 percent change in quantity of hops demanded in the same direction. The supply model showed that US acreage is significant at the 1 percent level. For every 1 percent change in US acreage, there will be a 0.889 percent change in quantity of hops supplied in the same direction. The implications of this study are viewed in relation to both producers and consumers.

The lack of effective demand forecasting strategies can result in imprecise inventory replenishment, inventory overstock, and unused inventory. The purpose of this single case study was to explore successful demand forecasting strategies that leaders of a small, retail, medical supply business used to increase profitability. The conceptual framework for this study was Winters's forecasting demand approach. Data were collected from semistructured, face-to-face interviews with 8 business leaders of a private, small, retail, medical supply business in the southeastern United States and the review of company artifacts. Yin's 5-step qualitative data analysis process of compiling, disassembling, reassembling, interpreting, and concluding was applied. Key themes that emerged from data analysis included understanding sales trends, inventory management with pricing, and seasonality. The findings of this study might contribute to positive social change by encouraging leaders of medical supply businesses to apply demand forecasting strategies that may lead to benefits for medically underserved citizens in need of accessible and abundant medical supplies.

The nature of geometric tasks that students engage with in classrooms influences the development of their geometric thinking. Although mathematics standards emphasize formal proofs and mathematical reasoning skills, geometric tasks in classrooms remain focused on students’ abilities to recall mathematical facts and use simple procedures rather than conceptual understanding. In order to facilitate students’ high-level mathematical thinking, teachers need to provide sufficient opportunities for students to engage in cognitively demanding mathematical tasks. The use of dynamic geometry software (DGS) in classrooms facilitates conceptual understanding of geometric proofs. The black box approach is a new type of task in which students interact with pre-constructed figures to explore mathematical relationships by dragging and measuring geometric objects. This approach is challenging to students because it “requires a link between the spatial or visual approach and the theoretical one” (Hollebrands, Laborde, & Sträßer, 2008, p. 172). This study examined how preservice secondary mathematics teachers make choices or create geometric tasks using DGS in terms of cognitive demand levels and how the black box approach influences the way preservice teachers conceptualize their roles in their lesson designs. Three preservice secondary mathematics teachers who took a semester-long mathematics teaching course participated in this qualitative case study. Data include two lesson plans, before and after instructions for geometric DGS tasks, pre- and post-interview transcripts, electronic files of geometric tasks, and reflection papers from each participant. The Mathematical Task Framework (Stein, Smith, Henningsen, & Silver, 2009) was used to characterize mathematical tasks with respect to level of cognitive demand. A Variety of geometric task types using DGS was introduced to the participants (Galindo, 1998). The dragging modalities framework (Arzarello, Olivero, Paola, & Robutti, 2002; Baccaglini-Frank & Mariotti, 2010) was employed to emphasize the cognitive demand of geometric tasks using DGS. The PURIA model situated the participants’ conceptualized roles in technology use (Beaudin & Bowers, 1997; Zbiek & Hollebrands, 2008). Findings showed that the preservice teachers only employed geometric construction types on low level geometric DGS tasks, which relied on technological step-by-step procedures students would follow in order to arrive at the same results. The preservice teachers transformed those low level tasks into high level tasks by preparing DGS tasks in advance in accordance with the black box approach and by encouraging students to explore the tasks by posing appropriate questions. However, as soon as they prepared high level DGS tasks with deductive proofs, low level procedure-based tasks followed in their lesson planning. The participants showed positive attitudes towards using DGS to prepare high level geometric tasks that differ from textbook-like procedural tasks. Major factors influencing preservice teachers’ preparation of high level tasks included teachers’ knowledge of mathematics, pedagogy, and technology, as well as ways of using curriculum resources and teachers’ abilities to set appropriate lesson goals. Findings of this investigation can provide guidelines for integrating DGS in designing high level geometric tasks for teacher educators, researchers, and textbook publishers.

The paper investigates how including the distribution of wealth changes the demand effects of redistributing functional income. It develops a model with an endogenous wealth distribution and shows that the endogenous rise in wealth inequality resulting from a redistribution towards profits weakens the growth effects of this redistribution. Consequently, a wage-led regime becomes more strongly wage-led. A profit-led regime on the other hand becomes less profit-led and there may even be a regime switch - in this case the short-run profit-led economy becomes wage-led in the long run due to the endogenous effects of wealth inequality. The paper thereby provides a possible explanation for the instability of demand regimes over time. / Series: Ecological Economic Papers

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This thesis investigates two research problems in analyzing floating car data (FCD): automated segmentation and privacy. For the former, we design an automated segmentation method based on the social functions of an area to enhance existing traffic demand analysis. This segmentation is used to create an extension of the traditional origin-destination matrix that can represent origins of traffic demand. The methods are then combined for interactive visualization of traffic demand, using a floating car dataset from a ride-hailing application. For the latter, we investigate the properties in FCD that may lead to privacy leaks. We present an attack on a real-world taxi dataset, showing that FCD, even though anonymized, can potentially leak privacy. / Graduate

There are a number of factors recognised as significant for nursing staff retention. These include, a lack of organisational care, bullying (commonly referred to as horizontal violence), and high workload acuity. However, there does not appear to be any indication that these factors influence the retention of nurses within the speciality of acute perioperative nursing. A descriptive study using postpositivist methodology and triangulation of methods was designed to answer the question: What are the critical factors that influence staff retention in an acute perioperative environment? Forty-eight (n = 48) perioperative nurses answered a questionnaire in relation to individual needs, provision of nursing care and administration and management. Four (n = 4) nurses subsequently participated in a focus group interview that explored in more depth, the survey data related to the following characteristics: Educational opportunities; Level of workload acuity; Rostering flexibility; Management; Established policies/Quality assurance; Graduate orientation programs and Professional relationships in an acute perioperative setting. Data analysis revealed that > 90% of respondents agreed that these characteristics are important for job satisfaction and influence staff retention in an acute perioperative environment. A sense of belonging appears to be the most important theme that emerged from the qualitative data. Job satisfaction and staff retention are attained when nurses have a sense of belonging in the workplace. To achieve these, nurses need to identify barriers, develop their communication and leadership skills and determine the ideal professional practice model. The themes (Figure 5), "Finding time" and increased "sick leave", in relation to workload acuity are new findings that provide a platform for future research.