Download or read book A D Vine Copula Based Quantile Regression Approach for the Prediction of Heating Energy Consumption Using Historical Data for German Households written by Rochus Niemierko and published by GRIN Verlag. This book was released on 2019-09-23 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2018 in the subject Economics - Statistics and Methods, grade: 1,0, University of Augsburg, language: English, abstract: The aim of this thesis is to add to the as of yet mostly missing literature on how a D-vine copula based quantile regression model can be used to predicte the accurate level of energy consumption. Energetic retrofitting of residential buildings is poised to play an important role in the achievement of ambitious global climate targets. A prerequisite for purposeful policy-making and private investments is the accurate prediction of energy consumption. Building energy models are mostly based on engineering methods quantifying theoretical energy consumption. However, a performance gap between predicted and actual consumption has been identified in literature. Data- driven methods using historical data can potentially overcome this issue. The D-vine copula-based quantile regression model used in this study achieved very good fitting results based on a representative data set comprising 25,000 German households. The findings suggest that quantile regression increases transparency by analyzing the entire distribution of heating energy consumption for individual building characteristics. More specifically, the analyses reveal the following exemplary insights. First, for different levels of energy efficiency, the rebound effect exhibits cyclical behavior and significantly varies across quantiles. Second, very energy-conscious and energy-wasteful households are prone to more extreme rebound effects. Third, with regards to the performance gap, heating energy demand of inefficient buildings is systematically underestimated, while it is overestimated for efficient buildings. Therefore, The remainder of this thesis is organized as follows. Section 2 presents a concise categorization of building energy models. Section 3 presents existing data-driven methods used for the pre-diction of heating energy consumption in the residential sector. Next, Section 4 elaborates on vine copula-based quantile regression. This is followed by a description of the data employed in Section 5. Section 6 presents the empirical results and Section 7 provides the practical im-plications and contribution of the quantile regression approach introduced. Finally, the conclu-sions and limitations of this thesis are discussed in Section 8.

Download or read book Revisiting Heat Energy Consumption Modeling written by Florian Heesen and published by . This book was released on 2018 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper offers new insights on utility-driven heat energy consumption. The research question addressed is whether economic aspects affect short-term, less conscious behavior in the same way as long-term, more conscious behavior. The model proposed is based on Becker's household production theory and integrates economic, engineering and behavioral elements. Comparative statics enables an interdisciplinary integration of price- and income functions to cover economic influences, the production function to cover technical influences, and the utility-based choice architecture. Based on a functional representation of the theories, a panel data model of heat energy consumption is estimated. The empirical analysis is based on data from 60 adjacent apartments in South-West Germany. We find empirical evidence that the price elasticity of demand is only statistically significant when using yearly aggregated data. This result provides evidence that occupants apparently do not act upon energy price signals when following their daily home heating routine. In less frequent considerations, as e.g. according to their yearly billing cycles, occupants adjust their heat energy consumption with respect to the fuel price influence. Furthermore, in relation to the other influences on heat energy consumption, we find that the price impact is less pronounced than the impact of comfort conditions.

Download or read book Regression Analysis of Energy Consumption by End Use written by Robert B. Latta and published by . This book was released on 1983 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predicting Residential Heating Energy Consumption and Savings Using Neural Network Approach written by Badr Ibrahim Al Tarhuni and published by . This book was released on 2019 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Upgrading and replacing inefficient energy-consuming equipment in both the residential and commercial building sectors offers a great investment opportunity, with significant impacts on economic, climate, and employment. Cost effective retrofits of residential buildings could yield annual electricity savings of approximately 30 percent in the United States. This obviously could reduce greenhouse gas emissions in the U.S. significantly. Further, investment in energy efficiency can create millions direct and indirect jobs throughout the economy for manufacturers and service providers that supply the building industry. Unfortunately, the prediction in savings, which has been generally based upon energy models, has been circumspect, with energy savings typically over-predicted. Investor confidence as a result can degrade. An enabler for this research is a collective grouping of over 500 residential buildings used for student housing owned by a Midwestern U.S. university. These residences offer significant variation in size, ranging from a floor area of 715 to 2800 square feet, in age, ranging from the early 1900s to new construction, and energy effectiveness, the latter occurring mostly as a result of improvements made gradually over time to some residences over the past fifteen years. The historical monthly natural gas and electricity energy consumption for these houses is available. Additionally, in the summer of 2015, energy and building data audits were completed on a total of 139 residences. Documented in these audits were the amount and type of insulation in the walls and attic, areas and types of windows, floor heights, maximum occupancy, appliance (refrigerator, range, oven) specifications, heating ventilation air-conditioning system specifications, domestic hot water equipment specifications, and the presence of a basement. Finally, county auditor real estate information was relied upon to obtain detailed features of each residence, including the age of the house, number of floors, floor area of each level, and total floor area. Using this data, a data mining approach based upon an artificial neural network (ANN) model was shown to be effective in estimating the annual heating energy savings from a variety of measures for a large number of houses for which energy characteristics are known and energy consumption data is available. In combination with cost models for implementation of the measures, the cost effectiveness of every measure considered for each residence was estimable. This preliminary study provides the starting point for the research presented here. With knowledge of the individual cost effectiveness of all measures within a collective grouping of residences, it becomes possible to adopt a strategy for energy reduction based upon a "worst to first" methodology. The economic impact of adoption of this methodology is then determined using an economic-input-output (EIO) approach. Considering only those measures that are economically viable and extrapolating the results from this study to the entire Dayton region yields with the initial energy efficiency investment of $26.1M can result in a total local economic impact of $41.2M (i.e. summation of direct, indirect, and induced) and additional economic impacts stemming from the annual energy savings of $2.21M for the lifetime of the considered EE measures.

Download or read book Heterogeneity in Residential Electricity Consumption written by Stephan Sommer and published by . This book was released on 2015 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining for Accurately Estimating Residential Natural Gas Energy Consumption and Savings Using a Random Forest Approach written by Adel Ali Naji and published by . This book was released on 2019 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cost effective energy efficiency improvements in residential buildings could yield annual electricity savings of approximately 30 percent within this sector for the United States. Furthermore, such investment can create millions of direct and indirect jobs throughout the economy. Unfortunately, realizing these savings is difficult. One of the impediments for realization is the means by which savings can be estimated. The prevalent approach is to use energy models to estimate. However, actual energy savings are more often than not over-predicted by energy models, leading to wariness on the part of potential investors which include the residents themselves. A driver for this research is 500 residential buildings with known geometrical and historical energy data owned by the University of Dayton. Further, the energy characteristics of these buildings are knowable. This housing stock offers significant diversity in size (ranging from a floor area of 715 to 2800 square feet), age (from the early 1900s to new construction) and energy effectiveness, the latter occurring as a result of gradual improvements made to residences over the past 15 years. In the summer of 2015 energy and building data audits were conducted on a subset of 139 homes. The audit documented the areas of the walls and attic, the amount and type of insulation in the walls and attic, areas and types of windows, floor heights, maximum occupancy, appliance (refrigerator, range, oven) specifications, heating ventilation air-conditioning system specifications domestic hot water equipment specifications, interior attic penetration area, and the presence of a basement. A data mining approach was used for developing the Random Forest (RF) model to predict energy consumption in a group of single family houses based upon knowledge of residential energy characteristics, historical energy consumption, occupancy and building geometrical data, as well as inferred energy characteristics from energy consumption data. The model was used to estimate savings and develop a cost implementation model from discrete measures for each residence. Thus, the cost effectiveness of each possible measure could be assessed. From these, prioritized energy reduction measures among all possible measures for all residences could be identified based upon a "worst-to-first" strategy in order to achieve community-scale energy (and carbon) savings most cost effectively. The results when extrapolated 45,000 single family houses in Dayton, Ohio show that a preliminary investment in energy efficiency of $26 million can achieve annual energy cost savings of $2.21M per year. As or more importantly, an Economic Input-Output analysis reveals a total sequential economic impact of $41.2M from the investment. Thus, this approach offers significant and indisputable local impact.

Download or read book Optimal Residential Energy Consumption Prediction and Analysis written by Joshua Daniel Rhodes and published by . This book was released on 2014 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the United States, buildings are responsible for 40.36 Quads (40.36 x 1015 BTU) of total primary energy consumption per year, 22.15 of which are used in residential buildings (reference year 2010). Also, the United States residential sector is responsible for about 20% of United States carbon emissions or about 4% of the world's total. While there are over 130 million residential units in the United States, only 0.1% of R&D is spent in the residential sector. This means the residential sector represents an underinvested opportunity for energy savings. Tackling that problem, this dissertation presents work that is focused on assessing, analyzing, and optimizing how residential buildings use and generate energy. This work presents an analysis of a unique dataset of 4971 energy audits performed on homes in Austin, Texas in 2009 - 2010. The analysis quantifies the prevalence of typical air-conditioner design and installation issues such as low efficiency, oversizing, duct leakage, and low measured capacity, then estimates the impacts that resolving these issues would have on peak power demand and cooling energy consumption. It is estimated that air-conditioner use in single-family residences currently accounts for 17 - 18% of peak demand in Austin, and that improving equipment efficiency alone could save up to 205 MW, or 8%, of peak demand. It was also found that 31% of systems in this study were oversized, leading to up to 41 MW of excess peak demand. Replacing oversized systems with correctly sized higher efficiency units has the potential for further savings of up to 81 MW. Also, the mean system could achieve 18% and 20% in cooling energy savings by sealing duct leaks and servicing air-conditioning units to achieve 100% of nominal capacity, respectively. A different dataset of measured whole-home electricity consumption from 103 homes in Austin, TX was analyzed to 1) determine the shape of seasonally-resolved residential demand profiles, 2) determine the optimal number of normalized representative residential electricity use profiles within each season, and 3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the k-means clustering algorithm. The number of groups within each season was determined by comparing 30 different optimal clustering criteria. Then probit regression was performed to determine if homeowner survey responses could serve as explanatory variables for the clustering results. This analysis found that Austin homes typically fall into one of two seasonal groups. Because these groups differ in temporal energy use and the wholesale electricity price is temporal, homes in one group use more expensive electricity than others. The probit regression results indicated that variables such as whether or not someone worked from home, the number of hours of television watched per week, and level of education have significant correlation with average profile shape, but that significant indicators of profile shape can vary across seasons. Also, these results point to markers of households that might be more impacted by time-of-use (TOU) or real time price (RTP) electricity rates and can act as predictors as to how changing local demographics can change local electricity demand patterns. This work also considers the effect of the placement (azimuth and tilt) of fixed solar PV systems on their total energy production, peak power production, and economic value given local solar radiation, weather, and electricity market prices and rate structures. This model was then used to calculate the output of solar PV systems across a range of azimuths and tilts to find the energetically and economically optimal placement. The result of this method, which concludes that the optimal placement can vary with a multitude of conditions, challenges the default due-south placement that is conventional for typical installations. For Austin, TX the optimal azimuth to maximize energy production is about 187 - 188°, or 7 - 8° west of south, while the optimal azimuth to maximize economic output based on the value of the solar energy produced is about 200 - 230° or 20 - 50° west of south. The differences between due south (which is the conventional orientation) and the optimal placement were on the order of 1 - 7%. For the rest of the US and for most locations the energetically optimal solar PV azimuth is within 10° of south. Considering the temporal value of the solar energy produced from spatially-resolved market conditions derived from local time-of-use rates, the optimal placement shifts to a west-of-south azimuth in attempt to align solar energy production with higher afternoon prices and higher grid stress times. There are some locations particularly on the west coast that have west-of-south energy optimal placements, possibly due to typical morning clouds or fog. These results have the potential to be significant for solar PV installations: utilities might alter rate structures to encourage solar generation that is more coincident with peak demand, utilities might also use west-of-south solar placements as a hedge against future wholesale electricity price volatility, building codes might encourage buildings to match roof azimuths with local optimal solar PV generation placements, and calculations of the true value of solar in optimal and non-optimal placements can be more accurate. This analysis also uses a dataset of whole home electricity consumption to consider the role of small distributed fuel cells in managing energy and thermal flows in the home. The analysis determines that the average home in Austin, TX could utilize a 5.5 kW fuel cell either for total generation or backup, and the average home could operate as its own micro-grid while not sacrificing core functionality. Matching the thermal output of a possibly smaller fuel cell, used in combined heat and power mode (CHP), to an absorption refrigeration system in place of traditional space cooling further reduces the needed capacity. Lastly, it is estimated that the system efficiency could possibly double by transporting natural gas to the end user to be converted into electricity and heat as compared with traditional methods of using natural gas for power generation followed by electricity delivery. Results from two regression analyses of total energy use and energy use reductions following energy efficiency retrofits are also presented. The first model shows that home size and age were positively correlated with total yearly energy use, but there is significant correlation of reduced yearly energy use with increased energy and water knowledge. This result might lend some support for increased energy and water education campaigns. The second model (retrofit analysis) also provided results that utilities can use to assess the value of residential retrofit rebates as compared to the cost of acquiring energy on the wholesale market. The second model indicates that the current level of rebates is cost effective for the utility (on a $ per kWh offset basis) for all three considered retrofits (air-sealing, attic insulation, and air-conditioner replacement) and the rebates could be increased while still being below the cost of acquiring electricity on the wholesale market. Considering an average of $0.113/kWh for residential electric service, both the air-sealing and increased attic insulation seem to make economic sense for the homeowner given current rebate structures.

Download or read book Statistical Postprocessing of Ensemble Forecasts written by Stéphane Vannitsem and published by Elsevier. This book was released on 2018-05-17 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place Provides real-world examples of methods used to formulate forecasts Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

Download or read book Energy Use Intensities Across Building Use Types and Climate Zones Using the CBECS Dataset written by Shreyas Mandar Kamath and published by . This book was released on 2020 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, we explored methods for predicting building natural gas heating use using electricity consumption data provided in the CBECS dataset. We performed numerous layers of subsets and built linear models at each stage predicting natural gas heating energy intensity (EI) as a function of cooling electric EI and electric EUI. We found that the R2 values never exceeded 0.1 and concluded that the CBECS dataset lacks sufficient predictive power for predicting building energy consumption. We applied the Kaiser-Meyer-Olkin test to the CBECS dataset to measure its sampling adequacy and obtained a value of 0.5 suggesting that it is "miserable" for factor analysis. Recommendations were made for improving the dataset by adding more predictors and increasing its sample size. Then, we introduced a novel approach for extracting meaningful information from this noisy dataset by using the Jackknife method for estimating the standard error on the mean and constructing large-sample confidence intervals on the mean EUI and EIs for various end uses for different building use types located in diverse climate zones. We introduced the concept of distinctly distinguishable climate zone pairings for assessing the influence of climate zone on heating and cooling EI for building use types. In addition, a magnitude-based tiered classification system for EUI and EIs for individual end uses was devised for ranking building use types. We discussed how this system can be used to identify and prioritize the targeting of specific areas for energy saving efforts for individual building use types. Retail and education buildings were found to exhibit the highest level of distinguishability, while food service, food sales, and inpatient healthcare buildings were found to have the highest mean EUIs with 83.4% CIs of [284, 330], [222, 263], and [194, 234] kBtu/sq.ft. respectively.

Download or read book Heating Ventilating and Air conditioning System Energy Demand Coupling with Building Loads for Office Buildings written by Ivan Korolija and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The UK building stock accounts for about half of all energy consumed in the UK. A large portion of the energy is consumed by nondomestic buildings. Offices and retail are the most energy intensive typologies within the nondomestic building sector, typically accounting for over 50% of the nondomestic buildings' total energy consumption. Heating, ventilating and air conditioning (HVAC) systems are the largest energy end use in the nondomestic sector, with energy consumption close to 50% of total energy consumption. Different HVAC systems have different energy requirements when responding to the same building heating and cooling demands. On the other hand, building heating and cooling demands depend on various parameters such as building fabrics, glazing ratio, building form, occupancy pattern, and many others. HVAC system energy requirements and building energy demands can be determined by mathematical modelling. A widely accepted approach among building professionals is to use building energy simulation tools such as EnergyPlus, IES, DOE2, etc. which can analyse in detail building energy consumption. However, preparing and running simulations in such tools is usually very complicated, time consuming and costly. Their complexity has been identified as the biggest obstacle. Adequate alternatives to complex building energy simulation tools are regression models which can provide results in an easier and faster way. This research deals with the development of regression models that enable the selection of HVAC systems for office buildings. In addition, the models are able to predict annual heating, cooling and auxiliary energy requirements of different HVAC systems as a function of office building heating and cooling demands. For the first part of the data set development used for the regression analysis, a data set of office building simulation archetypes was developed. The four most typical built forms (open plan sidelit, cellular sidelit, artificially lit open plan and composite sidelit cellular around artificially lit open plan built form) were coupled with five types of building fabric and three levels of glazing ratio. Furthermore, two measures of reducing solar heat gains were considered as well as implementation of daylight control. Also, building orientation was included in the analysis. In total 3840 different office buildings were then further coupled with five different HVAC systems: variable air volume system; constant air volume system; fan coil system with dedicated air; chilled ceiling system with embedded pipes, dedicated air and radiator heating; and chilled ceiling system with exposed aluminium panels, dedicated air and radiator heating. The total number of models simulated in EnergyPlus, in order to develop the input database for regression analysis, was 23,040. The results clearly indicate that it is possible to form a reliable judgement about each different HVAC system's heating, cooling and auxiliary energy requirements based only on office building heating and cooling demands. High coefficients of determination of the proposed regression models show that HVAC system requirements can be predicted with high accuracy. The lowest coefficient of determination among cooling regression models was 0.94 in the case of the CAV system. HVAC system heating energy requirement regression models had a coefficient of determination above 0.96. The auxiliary energy requirement models had a coefficient of determination above 0.95, except in the case of chilled ceiling systems where the coefficient of determination was around 0.87. This research demonstrates that simplified regression models can be used to provide design decisions for the office building HVAC systems studied. Such models allow more rapid determination of HVAC systems energy requirements without the need for time-consuming (hence expensive) reconfigurations and runs of the simulation program.

Download or read book Predictions of Monthly Energy Consumption and Annual Patterns of Energy Usage for Convenience Stores by Using Multiple and Nonlinear Regression Models written by Krisanee Muendej and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.

Download or read book Modeling Energy Consumption of a Multipurpose University Building Using Multiple Linear Regression written by Erin Leigh Volz and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Determinants and Trends in the Household Energy Consumption for Different End Uses in the United States During 2001 2009 written by Sadasivan Karuppusamy and published by . This book was released on 2013 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this study is a broad examination of household energy consumption for appliance use, space heating, space cooling, and water heating in United States over the period 2001-2009 using Residential Energy Consumption Survey (RECS) from the years 2001 and 2009. Methods: Linear Regression Analysis is used to identfy determinants of household energy consumption for each of the end uses. Regression based decomposition analysis is used to identify trends in residential energy consumption for each of the end uses. Results: The study identified current determinants of household energy consumption for each of the end uses. These determinants are employed in the study to predict trends in household energy consumption for each of the end uses. Based on the results policy interventions at local and federal level for energy conservation are suggested.

Download or read book As operated Heat Loss Coefficients of Residential Buildings in the Pacific Northwest written by and published by . This book was released on 1992 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Previous research of residential electrical space-heating data has revealed that the heat loss coefficients obtained from empirical data (''as-operated'' UAs) are, on average, about 25% below the UA calculated from the shell construction of each building. This as-operated UA is obtained from a linear regression of the measured space-heating energy consumption versus the inside-outside temperature difference. This finding indicates that simple steady-state calculation techniques for heating energy consumption utilizing only UAs may be inaccurate in estimating annual consumption. The purpose of this research was to study how climate, construction, and occupant variables may affect the as-operated UA and, therefore, the annual heating energy consumption. Specifically, the goal is to gain a greater understanding of how and why the as-operated UA differs from the construction-based nameplate UA. Multiple seasons of daily heating data from 131 occupied single-family residential sues were analyzed. A multiple linear regression was used to generate a model that utilizes the construction-based UAs and other characteristics of individual residences to predict an as-operated UA that better estimates annual heating energy.

Download or read book Projecting Household Energy Consumption Within a Conditional Demand Framework written by and published by . This book was released on 1991 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

Download or read book Elements of Copula Modeling with R written by Marius Hofert and published by Springer. This book was released on 2019-01-09 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others). Copulas are multivariate distribution functions with standard uniform univariate margins. They are increasingly applied to modeling dependence among random variables in fields such as risk management, actuarial science, insurance, finance, engineering, hydrology, climatology, and meteorology, to name a few. In the spirit of the Use R! series, each chapter combines key theoretical definitions or results with illustrations in R. Aimed at statisticians, actuaries, risk managers, engineers and environmental scientists wanting to learn about the theory and practice of copula modeling using R without an overwhelming amount of mathematics, the book can also be used for teaching a course on copula modeling.

Download or read book Analyzing Dependent Data with Vine Copulas written by Claudia Czado and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications. It focuses on statistical estimation and selection methods for vine copulas in data applications. These flexible copula models can successfully accommodate any form of tail dependence and are vital to many applications in finance, insurance, hydrology, marketing, engineering, chemistry, aviation, climatology and health. The book explains the pair-copula construction principles underlying these statistical models and discusses how to perform model selection and inference. It also derives simulation algorithms and presents real-world examples to illustrate the methodological concepts. The book includes numerous exercises that facilitate and deepen readers understanding, and demonstrates how the R package VineCopula can be used to explore and build statistical dependence models from scratch. In closing, the book provides insights into recent developments and open research questions in vine copula based modeling. The book is intended for students as well as statisticians, data analysts and any other quantitatively oriented researchers who are new to the field of vine copulas. Accordingly, it provides the necessary background in multivariate statistics and copula theory for exploratory data tools, so that readers only need a basic grasp of statistics and probability.