Thesis (Ph. D.)--Michigan State University. Dept. of Curriculum, Teaching, and Educational Policy, 2008. / Title from PDF t.p. (viewed on July 7, 2009) Includes bibliographical references (p. 105-109). Also issued in print.

Since wireless sensor networks are energy constrained, introducing a method that facilitates the efficient use of the available energy in each node is a fundamental design issue. In this work, a mechanism to monitor the residual energy of sensor networks is proposed. The information about the residual energy of each sensor node in the network is saved in a special node called monitoring node. This information can be used as input to other applications to prolong the network lifetime. Each sensor node in the network uses the proposed prediction-based model to forecast its energy consumption rate. The model's performance is measured based on the number of energy packets sent to the monitoring node for various thresholds (prediction errors). The simulation results showed that reducing the threshold will produce more accurate projection of the residual energy of each node in the monitoring node. However, as the threshold is further decreased the number of energy packets sent to the monitoring node grows significantly. This incurs higher energy map construction cost on the network in terms of energy and bandwidth. The simulation results also showed the tradeoff between increasing the accuracy of the prediction model and reducing the cost of energy map construction.

This research proposes to simplify the energy consumption assessment for residential homes while building the foundation towards the development of prediction tools that can achieve a credible level of accuracy for confident decision making. The energy consumption assessment is based on simplified energy consumption models. The energy consumption analysis uses a reduced number of energy model equations utilizing a critical, limited set of parameters. The results of the analysis are used to develop the minimum set of consumption influence parameters with predicted effects for each energy consumption domain. During this research study, multiple modeling approaches and occupancy scenarios were utilized according to climate conditions in Blacksburg, Virginia. As a part of the analysis process, a parameter study was conducted to: develop a comprehensive set of energy consumption influence parameters, identify the inter-relationships among parameters, determine the impact of energy consumption influence parameters in energy consumption models, and classify energy consumption influence parameters under identified energy consumption domains. Based on the results of the parameter study, a minimum set of parameters and energy consumption influence matrices were developed. This research suggests the minimum set of parameters with predicted effects to be used during the development of the simplified baseline energy consumption model. / Ph. D.

This dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles. The resulting few significant wavelet coefficients represent not only average but also variation of the weather components. A SOM is used for clustering and projecting high-dimensional data into usually a one or two dimensional map to reveal the data structure which is not clear by visual inspection. In this study, neighborhoods that contain days with similar meteorological conditions are classified by a SOM using significant wavelet coefficients; a baseline model is then developed for each neighborhood. In each neighborhood, modeling is more robust without unnecessary compromises that occur in global predictor regression models. This method was applied to the Energy Predictor Shootout II dataset and compared with the winning entries for hourly energy use predictions. A comparison between the "neighborhood" based linear regression model and the change-point model for daily energy use prediction was also performed. We also studied the application of the non-parametric nearest neighborhood points approach in determining the uncertainty of energy use prediction. The uncertainty from "local" system behavior rather than from global statistical indices such as root mean square error and other measures is shown to be more realistic and credible than the statistical approaches currently used. In general, a baseline model developed by local system behavior is more reliable than a global baseline model. The "neighborhood" based neural network model was found to predict building baseline energy use more accurately and achieve more reliable estimation of energy savings as well as the associated uncertainties in energy savings from building retrofits.

The proposed Sustainable Energy Model is based on rice husk and the development of briquettes made from agricultural waste, which will increase efficiency in the domestic sector, and potentially replace conventional polluting fuels such as firewood. Large volumes of rice husks from millers are found scattered in rural agricultural areas of the San Martin region of Peru, where people are exposed daily to the emissions of polluting gases produced by burning these wastes, causing respiratory and lung diseases. Despite present circumstances, this waste has a great energetic potential that is not yet used by society, representing an opportunity to encourage socioenvironmental development and generate added value to the husk. Based on a compaction and drying process, briquettes were obtained with 4,040 kcal / kg of heat power and 80.39% combustion efficiency, allowing the little use of biofuel compared to firewood, and consequently, the utilization of this biofuel would reduce levels of deforestation. In contrast to similar projects, the sustainability of an energetic model of briquette production will be achieved when economic, environmental and social aspects are met, developing clean technologies and an efficient supply chain, from the supply of the husk to the commercialization of briquettes

A growing number of smart items are entering our daily lives as the Internet of Things becomes increasingly prevalent. ICT device miniaturization introduces a brand-new material type called Communicating Material (CM). The term “communications material” refers to a single system that includes a material equipped with communication devices. In this paper, the main limitation of CM was studied - the issue of energy consumption. Due to the limited battery capacity of sensor nodes, the issue of network lifetime comes to the fore, emphasizing the importance of power management and optimization for each sensor node. The first and most important step in tackling this problem is to precisely estimate and calculate each node's power usage. In addition, the WSN's embeddedness in the material makes it challenging to replace batteries and measure network power consumption, necessitating the development of a different approach to power consumption estimation. Thus, our work explores all the different approaches to energy estimation in WSN and tries to choose the best method that fits our WSN platform.

The mining industry in South Africa has long been a crucial contributor to the Gross Domestic Product (GDP) starting in the 18th century. In 2010, the direct contribution towards the GDP from the mining industry was 10% and 19.8% indirect. During the last decade global financial uncertainty resulted in commodity prices hitting record numbers when Gold soared to a high at $1900/ounce in September 2011, and thereafter the dismal decline to a low of $1200/ounce in July 2013. Executives in these markets have reacted strongly to reduce operational costs and focussing on better production efficiencies. One such a cost for mining within South Africa is the Operational Expenditure (OPEX) associated with electrical energy that has steadily grown on the back of higher than inflation rate escalations. Companies from the Energy Intensive User Group (EIUG) witnessed energy unit prices (c/kWh) and their percentage of OPEX grow to 20% from 7% in 2008. The requirement therefore is for more accurate energy budget forecasting models to predict what energy unit price escalations (c/kWh) occur along with the required units (kWh) at mines or new projects and their impact on OPEX. Research on statistical models for energy forecasting within the mining industry indicated that the historical low unit price and its notable insignificant impact on OPEX never required accurate forecasting to be done and thus a lack of available information occurred. AngloGold Ashanti (AGA) however approached Deloittes in 2011 to conclude a study for such a statistical model to forecast energy loads on one of its operations. The model selected for the project was the Monte Carlo analysis and the rationale made sense as research indicated that it had common uses in energy forecasting at process utility level within other industries. For the purpose of evaluation a second regression model was selected as it is well-known within the statistical fraternity and should be able to provide high level comparison to the Monte Carlo model. Finally these were compared to an internal model used within AGA. Investigations into the variables that influence the energy requirement of a typical deep level mine indicated that via a process of statistical elimination tonnes broken and year are the best variables applicable in a mine energy model for conventional mining methods. Mines plan on a tonnage profile over the Life of Mine (LOM) so the variables were known for the given evaluation and were therefore used in both the Monte Carlo Analysis that worked on tonnes and Regression Analysis that worked on years. The models were executed to 2040 and then compared to the mine energy departments’ model in future evaluations along with current actuals as measured on a monthly basis. The best comparison against current actuals came from the mine energy departments’ model with the lowest error percentage at 6% with the Regression model at 11% and the Monte Carlo at 20% for the past 21 months. This, when calculated along with the unit price path studies from the EIUG for different unit cost scenarios gave the Net Present Value (NPV) reduction that each model has due to energy. A financial analysis with the Capital Asset Pricing Model (CAPM) and the Security Market Line (SML) indicated that the required rate of return that investors of AGA shares have is 11.92%. Using this value the NPV analysis showed that the mine energy model has the best or lowest NPV impact and that the regression model was totally out of line with expectations. Investors that provide funding for large capital projects require a higher return as the associated risk with their money increases. The models discussed in this research all work on an extrapolation principle and if investors are satisfied with 6% error for the historical 2 years and not to mention the outlook deviations, then there is significance and a contribution from the work done. This statement is made as no clear evidence of any similar or applicable statistical model could be found in research that pertains to deep level mining. Mining has been taking place since the 18th century, shallow ore resources are depleted and most mining companies would therefore look towards deeper deposits. The research indicates that to some extent there exist the opportunity and some rationale in predicting energy requirements for deep level mining applications. Especially when considering the legislative and operational cost implications for the mining houses within the South African economy and with the requirements from government to ensure sustainable work and job creation from industry in alignment with the National Growth Path (NGP). For this, these models should provide an energy outlook guideline but not exact values, and must be considered along with the impact on financial figures. / MBA, North-West University, Potchefstroom Campus, 2014

The mining industry in South Africa has long been a crucial contributor to the Gross Domestic Product (GDP) starting in the 18th century. In 2010, the direct contribution towards the GDP from the mining industry was 10% and 19.8% indirect. During the last decade global financial uncertainty resulted in commodity prices hitting record numbers when Gold soared to a high at $1900/ounce in September 2011, and thereafter the dismal decline to a low of $1200/ounce in July 2013. Executives in these markets have reacted strongly to reduce operational costs and focussing on better production efficiencies. One such a cost for mining within South Africa is the Operational Expenditure (OPEX) associated with electrical energy that has steadily grown on the back of higher than inflation rate escalations. Companies from the Energy Intensive User Group (EIUG) witnessed energy unit prices (c/kWh) and their percentage of OPEX grow to 20% from 7% in 2008. The requirement therefore is for more accurate energy budget forecasting models to predict what energy unit price escalations (c/kWh) occur along with the required units (kWh) at mines or new projects and their impact on OPEX. Research on statistical models for energy forecasting within the mining industry indicated that the historical low unit price and its notable insignificant impact on OPEX never required accurate forecasting to be done and thus a lack of available information occurred. AngloGold Ashanti (AGA) however approached Deloittes in 2011 to conclude a study for such a statistical model to forecast energy loads on one of its operations. The model selected for the project was the Monte Carlo analysis and the rationale made sense as research indicated that it had common uses in energy forecasting at process utility level within other industries. For the purpose of evaluation a second regression model was selected as it is well-known within the statistical fraternity and should be able to provide high level comparison to the Monte Carlo model. Finally these were compared to an internal model used within AGA. Investigations into the variables that influence the energy requirement of a typical deep level mine indicated that via a process of statistical elimination tonnes broken and year are the best variables applicable in a mine energy model for conventional mining methods. Mines plan on a tonnage profile over the Life of Mine (LOM) so the variables were known for the given evaluation and were therefore used in both the Monte Carlo Analysis that worked on tonnes and Regression Analysis that worked on years. The models were executed to 2040 and then compared to the mine energy departments’ model in future evaluations along with current actuals as measured on a monthly basis. The best comparison against current actuals came from the mine energy departments’ model with the lowest error percentage at 6% with the Regression model at 11% and the Monte Carlo at 20% for the past 21 months. This, when calculated along with the unit price path studies from the EIUG for different unit cost scenarios gave the Net Present Value (NPV) reduction that each model has due to energy. A financial analysis with the Capital Asset Pricing Model (CAPM) and the Security Market Line (SML) indicated that the required rate of return that investors of AGA shares have is 11.92%. Using this value the NPV analysis showed that the mine energy model has the best or lowest NPV impact and that the regression model was totally out of line with expectations. Investors that provide funding for large capital projects require a higher return as the associated risk with their money increases. The models discussed in this research all work on an extrapolation principle and if investors are satisfied with 6% error for the historical 2 years and not to mention the outlook deviations, then there is significance and a contribution from the work done. This statement is made as no clear evidence of any similar or applicable statistical model could be found in research that pertains to deep level mining. Mining has been taking place since the 18th century, shallow ore resources are depleted and most mining companies would therefore look towards deeper deposits. The research indicates that to some extent there exist the opportunity and some rationale in predicting energy requirements for deep level mining applications. Especially when considering the legislative and operational cost implications for the mining houses within the South African economy and with the requirements from government to ensure sustainable work and job creation from industry in alignment with the National Growth Path (NGP). For this, these models should provide an energy outlook guideline but not exact values, and must be considered along with the impact on financial figures. / MBA, North-West University, Potchefstroom Campus, 2014

Master of Science / Department of Architectural Engineering / Charles L. Burton / An energy analysis was performed on a Midwestern residence to evaluate its performance based on energy use. A model of the actual house was replicated using eQuest and adjusted until its projected utility bills matched the actual yearly bills. This model was used to gauge how potential improvements made to the envelope and HVAC systems lowered the energy use. The results were documented after each improvement the feasible options were considered. The top alternatives were then combined to see how much money could be saved through renovating an existing home or through constructing a new residence. The overall goal of this report was to use the resulting improvement data as a reference for homeowners or home builders who are interested in conserving energy and money through residential improvements.

Stress kan vara positivt och negativt. Lite stress kan skärpa sinnet och förmågan att arbeta, men en för lång period med stress är negativt och skadligt. Stress ökar i samhället, inte minst inom yrken som vård och omsorg. Denna studie syftar till att undersöka hur stress, privat stöd och energi i arbetslivet ser ut på tre äldreboenden. I studien undersöks även hur olika strategier kan påverka och förbättra enskilda individers stresspåverkan. En kvantitativ tvärsnittsstudie baserad på enkäter gör det empiriska underlaget. Tre äldreboenden har studerats för att undersöka sambandet mellan energi, privatstöd och stress. Studien visar att medarbetarna på äldreboendena känner sig slutkörda med höga krav och låg kontroll enligt stress- och energi modellen. Studien påvisar ett negativt samband mellan privat stöd och stress. När en deltagares privata stöd är högt är stressen låg men för de individer där privata stödet är lågt är istället stressen hög. Inga andra samband kan påvisas. / Stress can be positive and negative. A little stress can sharpen the mind and the ability to work, but a too long period of stress is negative and harmful. The stress is increasing in society, especially in professions as nursing and care. This study aims to examine how stress, private support and energy at work looks at three homes for the elderly. The study also explores how different strategies can influence and improve individuals' stress effects. A quantitative cross-sectional study based on surveys makes the empirical basis. Three elderly have been studied to examine the relationship between energy, private support and stress. The study shows that employees of nursing homes feel exhausted with high demands and low control by the stress and energy model. The study demonstrates a negative relationship between private support and stress. When a participant private support is high is the stress low but for those individuals where private support is low the stress is high. No other connection can be demonstrated.