Regression analysis of energy consumption by end use by Robert B Latta

Cover of: Regression analysis of energy consumption by end use | Robert B Latta

Published by Energy Information Administration, Office of Energy Markets and End Use, Energy End Use Division, Supt. of Docs., U.S. G.P.O. [distributor] in Washington, D.C .

Written in English

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Subjects:

  • Energy consumption -- United States -- Statistical methods

Edition Notes

Book details

Other titlesResidential energy consumption survey
Statementprepared by Robert B. Latta
ContributionsUnited States. Office of Energy Markets and End Use. Energy End Use Division
The Physical Object
Paginationv, 116 p. ;
Number of Pages116
ID Numbers
Open LibraryOL14886461M

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Get this from a library. Regression analysis of energy consumption by end use. [Robert B Latta; United States. Office of Energy Markets and End Use. Energy End Use Division.].

Regression analysis of energy consumption by end use / prepared by Robert B. Latta Energy Information Administration, Office of Energy Markets and End Use, Energy End Use Division: Supt. of Docs., U.S. G.P.O. [distributor] Washington, D.C End-use Data and Analysis.

developed a multivariate linear regression model to predict energy use intensity during the retrofitting of buildings in the United States. Energy consumption. sheet data, the correlation between energy consumption and some basic parameters such as GDP, population growth, and industrial growth rate is analyzed through a regression model.

Regarding the model, energy consumption in is predicted as Mtoe. The supermarket studied and the methodology of the analysis and modelling is detailed in this section. As Fig. 1 indicates, this assessment is based on the actual consumption data, dry-bulb temperature and relative humidity records for This data was divided into two data sets to be used in a multiple linear regression analysis to generate two equations, one for electricity and one for gas.

The considerable amount of energy consumption associated to the residential sector justifies and supports energy consumption modeling efforts. Among the three approaches to develop energy models, statistical approaches are a good option to avoid the burden associated to engineering approaches when observed/measured data is available.

Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines May International Journal of Electrical Power & Energy.

This is quite important in developing countries such China and Iran severe fluctuation for energy consumption. Where classic regression approaches do not provide a suitable prediction.

In the present study, monthly data for electricity consumption in Iran are studied from to   The energy consumption of any industrial energy use is dependent upon a large number of variables [3].

In this context, continuous energy analysis is essential and predicting energy consumption is important for detect malfunctions and inefficiencies, for tracking of industrial machines and for proposing energy saving measures. Research on building energy demand forecasting using Machine Learning methods.

Features Gaussian process regression, also includes linear regression, random forests, k-nearest neighbours and support vector regression. Three projects posted, a online web tool, comparison of five machine learning techniques when predicting energy consumption of a campus building and a.

Annual electricity consumption was strongly related to the selected variables, with adjusted regression coefficients, adj. R 2, equal to for residential consumption, for non-residential consumption, and for total consumption.

The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement.

"Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. The set of variables under analysis received special attention also.

Apart from architectural and operational variables there are many parameters that influence the energy consumption of buildings. The variables selected were those that presented higher impact on the energy consumption, based on typical typology of Brazilian office buildings.

We recently got a question from one of our friends on Facebook about stepwise regression. I’m new to stepwise regression myself, and I turned to Regression analysis of energy consumption by end use book Minitab training manual for a little help in trying to explain this analysis.

I found an interesting example about identifying the major sources of energy usage at a manufacturing Regression analysis of energy consumption by end use book that I thought might be helpful to share. Load Forecasting: Partial End Use The end-use method determines energy demand through total kWh use from all of the electrical appliances used.

In the basic form, this model is simple accounting procedure that enumerates the end uses and adds the electricity use for each end use. Regression analysis is a statistical tool that allows you to normalise the impact that weather has on your energy usage.

Use of the tool ensures that fluctuating weather conditions will not compromise your energy saving figures. Simply put, rather than comparing one year’s usage to another’s, we regress your energy usage against weather.

We can see that while model’s R-squared value is quite low, it captures most of the energy consumption behavior of the facility, and so can be safely used for energy use prediction. So, the next time you run a regression analysis on energy data, calculate its CV(RMSE) to understand the model’s predictive accuracy.

U.S. Energy Information Administration | Residential Energy Consumption Survey (RECS) End-Use Models FAQs 6 normalization is done by multiplying each estimated end use with the ratio of the actual total consumption to the estimated total.

The results of this calculation become the final end -use consumption estimates for the household. The simulation output of interest was the total building energy consumption (OP). As sensitivity tends to follow the end-use com-ponents that consume the most energy, it is believed that input de-sign variables affecting these components will have significant influence on the total building energy consumption.

This article shows engineers and energy data analysts how to “normalize” energy consumption or production to account for the variation in weather over time. By the end of the article, you should understand why normalizing for weather is important, and how it can be done, either in a spreadsheet or using a free tool called RETScreen® Plus.

Renewable energy consumption (% of total final energy consumption) World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.

It is a regression analysis based tool that can help the plant and corporate managers establish a normalized baseline of energy consumption, track annual progress of intensity improvements, energy savings, Superior Energy Performance (SEP) EnPIs, and other EnPIs that account for variations due to weather, production, and other variables.

Corrections. All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:vyipSee general information about how to correct material in RePEc. For technical questions regarding this item, or to correct its authors, title.

An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance.

The regression analysis below relates US annual energy consumption in trillions of BTUs to the independent variable "US Gross Domestic Product (GDP) in trillions of dollars." The coefficient on the independent variable tells us that: For every additional trillion dollars of GDP, average energy consumption increased by 3, BTUs.

Neto, A.H., Fiorelli, F.A.S.: Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build. 40(12), – () CrossRef Google Scholar. You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read.

Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. Results. Our analysis (Fig.

1 and SI Appendix, Section 2) shows that gasoline price and population density correlate most strongly with transport energy use and GHG emissions, followed by economic contrast, the effect from economic activity dominates final energy consumption and is followed in importance by climatic variables [heating degree days (HDDs)], household size, and.

sector energy consumption as well as macro-variables, such as macroeconomic indicators, climatic 2 Reproduced from Swan, Lukas G., and V. Ismet Ugursal. “Modeling of End-Use Energy Consumption in the Residential Sector: A Review of Modeling Techniques.” Residential Energy Consumption Top-down Econometric Technological Bottom-up.

The energy consumption of office buildings in China has been growing significantly in recent years. Obviously, there are significant relationships between building envelope and the energy consumption of office buildings. The 8 key building envelope influencing factors were found in this paper to evaluate their effects on the energy consumption of the air-conditioning system.

With the exception of the exercises at the end of Section(x\) on a winter day and the energy consumption \(y\) of the furnace (8\) shoe for men is not the same size as a size \(8\) shoe for women.

Thus it would not be meaningful to apply regression analysis to Large Data Set 3. Nevertheless, compute the scatter diagrams, with. @article{osti_, title = {Energy use in petroleum refineries}, author = {Haynes, V.

O.}, abstractNote = {Refining petroleum accounts for about 4 percent of the total energy consumed in the United States and about 15 percent of all industrial consumption. The kinds of energy used and the manner in which energy is used are discussed on a process-by-process basis. THE MODEL BEHIND LINEAR REGRESSION 0 2 4 6 8 10 0 5 10 15 x Y Figure Mnemonic for the simple regression model.

than ANOVA. If the truth is non-linearity, regression will make inappropriate predictions, but at least regression will have a chance to detect the non-linearity.

Surveys of actual states of average energy consumption per unit show that 1) the largest component of energy consumption is room heating, followed by electricity, hot water supply, and gas, in that order; 2) energy consumption increases with household area; 3) among exposures, energy consumption is highest in households facing east or northeast.

First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal.

Understanding Industrial Energy Use through Sliding Regression Analysis Carl W. Eger III, City of Cleveland Kelly Kissock,University of Dayton, Department of Mechanical and Aerospace Engineering ABSTRACT Due to rising energy costs and global climate change, many industries seek to improve their energy efficiency.

The purpose of this study was to develop two new 2-regression models (2RM), for use in children that estimate energy expenditure (EE) using the ActiGraph GT3X: 1) mean vector magnitude (VM) counts or 2) vertical axis (VA) counts. The new 2RMs were also compared to. In particular, the consumption of gas required for heating homes depends on the temperature outside.

The company gathered data on the average daily consumption of gas in megajoules (Mj) and the average daily temperature in the area (in degrees Celsius) from March to October.

Output related to regression analysis of these data shown below. space heating energy consumption shares are based upon the cross-section regression models applied to the various Residential Energy Consumption Surveys by the EIA.

Inthe Office of Energy Efficiency and Renewable Energy (EERE), began to develop a system of energy intensity indicators for each of the broad end-use sectors of the U.S. PURPOSE: The purpose of this study was to develop two new two-regression models (2RM), for use in children, that estimate energy expenditure (EE) using the ActiGraph GT3X: 1) mean vector magnitude (VM) counts or 2) vertical axis (VA) counts.

The new 2RMs were also compared with existing ActiGraph equations for children.The U.S. Consumption Analysis: Using a Linear Regression Model By Songyi Paik April Abstract Recent U.S.

consumption has decreased, although it is the most significant factor in economic growth. Using a linear regression model, this paper shows that consumption is influenced by disposable income, oil price, and recession, but is.Heywood, J. () Assessing the Fuel Consumption and GHG of Future In-Use Vehicles.

PEA-AIT International Conference on Energy and Sustainable Development: Issues and Strategies (ESD ). Chiang Mai, Thailand. June PDF.

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