Model of US Army Materiel Command (AMC) energy consumption. Volume 1. Development of monthly energy-consumption equations. Final report
This report describes the development of equations to relate monthly energy consumption at U.S. Army Materiel Command (AMC) installations to weather and process parameters. Equations were developed using multiple linear regression analysis for the Armament Munitions and Chemical Command (AMCCOM) and Depot Systems Command (DESCOM) major subcommands of AMC. Multiple regression analysis is the process of fitting a curve to a set of data points. This technique, commonly known as least-squares curve fitting, is based on minimizing the sum of the squares of the errors between the data and the fitted equation. Once the regression analysis is performed, it is possible to generate confidence limits about the fitted equation. For example, the 95% confidence limits determine the range of data values that will fall within the limits 95% of the time. The confidence limits are useful in making statistically valid statements about the meaning of future observations. Accuracies of both the individual and the command-level equations are described, and examples for calculating confidence limits of the equations are given. Results in using the equations to predict AMCCOM and DESCOM total energy consumption indicate they provide a useful tool for managing AMC energy use. Lumped data regression was used to analyze energy-consumption data for AMCCOM, and the efforts are now under way to apply it to DESCOM data.
- Research Organization:
- Army Construction Engineering Research Lab., Champaign, IL (USA)
- OSTI ID:
- 5590857
- Report Number(s):
- AD-A-167366/4/XAB; CERL-TR-E-86/02-VOL-1
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
290100 -- Energy Planning & Policy-- Energy Analysis & Modeling
298000* -- Energy Planning & Policy-- Consumption & Utilization
ACCURACY
AMMUNITION
ENERGY CONSUMPTION
EQUATIONS
MATHEMATICS
MILITARY FACILITIES
REGRESSION ANALYSIS
SEASONAL VARIATIONS
STATISTICS
VARIATIONS
WEAPONS
WEATHER