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Research Papers

Next-Day Daily Energy Consumption Forecast Model Development and Model Implementation

[+] Author and Article Information
Li Song

 University of Oklahoma, 865 Asp Avenue, Norman, OK 73019lsong@ou.edu

Ik-seong Joo

BENEFF, LLC, 2621 Wexford Ct., Norman, OK 73072ijoo@beneff.com

Subroto Gunawan

Ameresco, Inc., 7929 Brookriver drive, Dallas, TX 75247sgunawan@ameresco.com

J. Sol. Energy Eng 134(3), 031002 (Apr 04, 2012) (8 pages) doi:10.1115/1.4006400 History: Received January 24, 2011; Revised March 07, 2012; Published April 03, 2012; Online April 04, 2012

Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production in order to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demand is a critical issue. Reduction of on- and off-peak demand can also extend the life span and defer or eliminate the replacement of power transformers. Next day electricity consumption is a critical set point to operate chillers and associated pumps at the appropriate time. In this paper, a data evaluation process using the annual daily average cooling consumption of a building was conducted. Three real-time building load forecasting models were investigated: a first-order autoregressive model (AR(1)), an autogressive integrated moving average model (ARIMA(0,1,0)), and a linear regression model. A comparison of results shows that the AR(1) and ARIMA(0,1,0) models provide superior results to the linear regression model, except that the AR(1) model has a few unacceptable spikes. A complete control algorithm integrated with a corrected AR(1) forecast model for a chiller plant including chillers, thermal storage system, and pumping systems was developed and implemented to verify the feasibility of applying this algorithm in the building automation system. Application results are also introduced in the paper.

Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Time series plot of daily average cooling consumption

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Figure 2

Sample ACF of daily average cooling consumption (with 5% significance limit as shown by the dashed lines)

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Figure 3

PACF of daily average cooling consumption (with 5% significance limit as shown by the dashed lines)

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Figure 4

Time series plot of first difference

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Figure 5

Sample ACF of the first difference (with 5% significance limit as shown by the dashed lines)

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Figure 6

PACF of the first difference (with 5% significance limit as shown by the dashed lines)

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Figure 7

Daily average cooling versus outside air temperature

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Figure 8

Comparison of fitting results of three models with the actual values (Test 1)

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Figure 9

Comparison of relative forecast errors

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Figure 10

Comparison of forecast results of three models and actual values (Test 2)

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Figure 11

Comparison of relative forecast errors

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Figure 13

Measured load curve (kW) in summer (measured June 8, 2006)

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Figure 14

Diagram of control sequence

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Figure 15

Daily electricity profile comparison (base case profile on May 29 and the improved profile on October 5)

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Figure 16

Outside air temperature comparison

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Figure 17

Long-term peak demand comparison

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