A Methodology to Quantify Residential Energy-Efficiency in a Heating-Dominated Climate

2011 ◽  
Author(s):  
Sean Casey ◽  
Marcus Bianchi ◽  
David Roberts ◽  
Moncef Krarti
Keyword(s):  
Energy Efficiency ◽  
Natural Gas ◽  
Inverse Modeling ◽  
Weather Data ◽  
Mechanical Equipment ◽  
Energy Assessment ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
The Difference

A methodology is presented that uses readily available information such as energy consumption data, limited building characteristics, and local daily temperature data to identify energy-inefficient homes in a heating-dominated climate. Specifically, this methodology is applied to 327 owner-occupied, single-family homes in Boulder, Colorado, which are compared to simulated prototype homes. A home’s energy-efficiency is characterized by its construction properties, such as insulation R-values, infiltration rates, and mechanical equipment efficiencies. Previous research indicates a close relationship between these properties and inverse modeling parameters, such as the heating slope (HS) values from variable-base degree-day (VBDD) models. The methodology compares the HS values from VBDD models of monthly natural gas consumption data to simulated HS values of reference homes. The difference, ΔHS, is the primary criterion for quantifying a home’s energy-efficiency and energy retrofit potential. To validate the results of the methodology, the results from a detailed energy assessment of a field-test home are used. Using the natural gas consumption noted in the utility data and historical weather data for the dates of bill, a VBDD model is created and the HSfield-test is calculated. HSreference of a 2009-IECC reference home of identical size is calculated and the difference, ΔHS, is calculated. Using UA-values and mechanical efficiencies from the energy assessment report, the theoretical HS values are calculated for both the assessed home and the reference home. The difference, ΔHStheoretical, is calculated. Overall, a 24% difference is found between the ΔHS and ΔHStheoretical. While the accuracy can be improved, the implication is that the energy-efficiency of homes can be inferred from inverse modeling of utility data under a specific set of conditions.

scholarly journals Analysis of Natural Gas Consumption by the Industrial Sector of Bangladesh

2013 ◽  
Vol 27 ◽  
pp. 1-7
Author(s):  
Mahbubur Rahman ◽  
Mohammad Tamin ◽  
Lutfar Rahman
Keyword(s):  
Natural Gas ◽  
Chemical Engineering ◽  
Industrial Sector ◽  
Policy Makers ◽  
Usual Practice ◽  
Consumption Data ◽  
Depth Analysis ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
First Time

The natural gas consuming sectors in Bangladesh are: i) Power, ii) Fertilizer, iii) Industry, iv) Captive power, v) Domestic, vi) Commercial, and vii) Transportation (CNG). Broad sectoral consumptions are reported in various literatures and reports, however, further breakdown of the data are difficult to find, and neither reported. The combined consumption of fertilizer, industry and captive power sectors is a significant portion of national gas consumption. This paper presents for the first time an in-depth analysis of the industrial sector gas consumption. Data were collected for each type of industry, and grouped according to the United Nations Framework Convention for Climate Change (UNFCCC). Captive generation is included in the industrial sector consumption, unlike the usual practice of considering it under the power generation. It is noticed that garments, textile and leather industries together have shown remarkable growth in the last decade. All the industries are more or less related to the national GDP growth. Some are export oriented while others address the internal market. Therefore analysis presented here should be helpful for policy makers to prioritize the sectors in case preferential supply and tariff adjustments become necessary.DOI: http://dx.doi.org/10.3329/jce.v27i1.15846 Journal of Chemical Engineering, IEB Vol. ChE. 27, No. 1, June 2012: 1-7

Statistical models for disaggregation and reaggregation of natural gas consumption data

2014 ◽  
Vol 42 (5) ◽  
pp. 921-937 ◽  
Author(s):  
M. Brabec ◽  
O. Konár ◽  
M. Malý ◽  
I. Kasanický ◽  
E. Pelikán
Keyword(s):  
Natural Gas ◽  
Statistical Models ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption

scholarly journals Analysis of methods and techniques for prediction of natural gas consumption

2019 ◽  
Vol 43 (1) ◽  
pp. 99-117 ◽  
Author(s):  
Dario Šebalj ◽  
Josip Mesarić ◽  
Davor Dujak
Keyword(s):  
Natural Gas ◽  
Prediction Models ◽  
Weather Data ◽  
Support Vector ◽  
Systematic Analysis ◽  
Scientific Databases ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Input Variables ◽  
Overview Analysis

Due to its many advantages, demand for natural gas has increased considerably and many models for predicting natural gas consumption are developed. The aim of this paper is to present an overview and systematic analysis of the latest research papers that deal with predictions of natural gas consumption for residential and commercial use from the year 2002 to 2017. Literature overview analysis was conducted using the two most relevant scientific databases Web of Science Core Collection and Scopus. The results indicate neural networks as the most common method used for predictions of natural gas consumption, while most accurate methods are genetic algorithms, support vector machines and ANFIS. Most used input variables are past natural gas consumption data and weather data, and prediction is most commonly made on daily and annual level on a country area level. Limitations of the research raise from relatively small number of analyzed papers but still research could be used for significant improving of prediction models for natural gas consumption.

Сonceptual approach to an efficient natural gas consumption strategy for an industrial enterprise

2020 ◽  
Vol 15 (4) ◽  
pp. 587-606
Author(s):  
Vladimir Alekseevich Koksharov ◽  
◽  
Irina Arturovna Kirshina ◽  
Keyword(s):  
Energy Efficiency ◽  
Natural Gas ◽  
Business Model ◽  
Energy Efficient ◽  
Industrial Enterprise ◽  
Efficient Strategy ◽  
Industrial Enterprises ◽  
Strategic Goals ◽  
Gas Consumption ◽  
Natural Gas Consumption

Currently, the development of industrial manufacturing and the energy consumption management for the natural gas demand better methodological tools to evaluate the efficiency of the enterprise’s energy strategy because modern methodological grounds for energy efficiency analysis do not account for a number of factors which affect the usage of secondary energy resources and do not offer any reasonable management solutions aimed to improve the efficiency of natural gas consumption in manufacturing processes. Current conceptual approaches to the evaluation of the natural gas consumption strategy by an industrial enterprise do not comprehensively consider the interaction between the enterprise’s energy supply in-house factors and the factors of energy resource market environment. Along with that, developing a strategy for the efficient natural gas consumption turns out to be the key factor to increase the competitiveness of an industrial enterprise, to guarantee a sustainable economic growth of industry, and to improve the ecological situation in the country. The purpose of the present research is to develop a comprehensive approach to natural gas consumption strategy for an industrial enterprise, this approach including a business model for strategy implementation and efficiency evaluation tools. The novelty of the research is as follows: 1) the studies in the energy efficient strategy are offered to apply new notions such as strategic competencies of energy efficiency and business competencies of energy efficiency which were used to justify author’s mathematical tools for the development of energy efficiency strategy for natural gas consumption and its business model; 2) the paper proposes an evaluation method for energy efficient strategy in natural gas consumption and its business model which determines the proportions based on the key indicators of enterprise’s energy efficiency. A well-proportioned system of these indicators is developed from the strategic competencies of the enterprise’s energy efficiency and includes the assessment of energy efficient, economic, financial, ecological sustainability. The key purpose of strategy’s development is seen to be a ready-to-use management system for the efficient natural gas consumption at an industrial enterprise, the system arising from the sustainable, dynamic, and innovative energy consumption economy which meets all modern requirements of the energy efficient development in industrial manufacturing. Compilation and implementation of natural gas consumption strategy within the proposed system of well-proportioned indicators is examined under three criteria: compliance, competitiveness, and efficiency. The system of well-proportioned indicators includes a strategic management map for the efficient natural gas consumption. This map visualizes the strategic goals and the key tasks of the energy efficient strategy. The author’s model of the energy efficient strategy as a system of well-proportioned dynamic indicators can be applied by the national industrial enterprises to identify the areas and to reason the management decisions aimed to improve the efficiency of natural gas consumption in manufacturing. The author’s conceptual approach to the strategy of the efficient natural gas consumption was tested at the metallurgic enterprises in Chelyabinsk region. Test results prove the trend in extending the evaluation of the efficiency of the natural gas consumption strategy, which could support the coherence of the strategic goals for the energy efficiency management at different levels of economy hierarchy: country, region, industry, and an industrial enterprise. The research reveals the connection between the strategy, its business model, and the manufacturing efficiency when achieving the goals of the enterprise’s energy efficient strategy. It has been justified that the efficiency manufacturing organization provides a real basis for the natural gas consumption strategy and requires additional investments into the advanced energy saving and energy efficient technologies. In its turn, this intensifies the extended recovery of the fixed assets at the national enterprises. Further research is seen to be focused on the analysis of the trends in developing the energy efficient strategies for natural gas consumption at the industrial enterprises, on improvement of the methodological tools for quantitative analysis of the energy efficient strategy impact on energy, economy, and ecological safety, as well as on the development of management impacts system providing the efficiency of natural gas consumption at the industrial enterprises.

scholarly journals Identifying Inefficient Single-Family Homes With Utility Bill Analysis

2010 ◽  
Author(s):  
Sean Casey ◽  
Moncef Krarti ◽  
Marcus Bianchi ◽  
David Roberts
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Diagnostic Procedures ◽  
Simulation Software ◽  
Single Family ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
A Site ◽  
International Energy Conservation Code

Differentiating between energy-efficient and inefficient single-family homes on a community scale helps identify and prioritize candidates for energy-efficiency upgrades. Prescreening diagnostic procedures can further retrofit efforts by providing efficiency information before a site-visit is conducted. We applied the prescreening diagnostic is applied to a simulated community of homes in Boulder, Colorado and analyzed energy consumption data to identify energy-inefficient homes. A home is defined as efficient if it is compliant with the prescriptive measures of the 2009 International Energy Conservation Code (IECC-2009) for Boulder, Colorado. Previous research indicates a correlation between building operational efficiency and the Heating Slope (HS) regression parameter resulting from the variable-base degree day method. We compared the HS values across a community of houses and those of an IECC-2009-compliant home to identify energy-inefficient homes on a community-scale. To simulate community-wide HS identification, we used DOE-2 energy simulation software for defined home archetypes and corresponding occupant behavior to artificially generate 567 sets of monthly natural gas consumption data Home archetypes were either compliant or incompliant at three conditioned areas; occupant effects were also simulated. Each simulation produced twelve months of natural gas use data. We used monthly energy consumption datasets to estimate the HS values with regression analysis and sorted the homes based on HS values.

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