Advanced Modeling of CO2 Emissions in Gas Furnaces Using AIC and BIC Methods

Dec 15, 2025

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This study explores a transfer function model to assess CO2 emissions in a gas furnace, examining input gas rates and outlet gas concentrations. Utilizing a dataset from Box & Jenkins with observations at 9-second intervals, we analyze the relationship between inputs and CO2 emissions. The paper compares alternate models through the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), highlighting the significance of statistical methods in evaluating model fit and parameter efficiency in time-series analysis.

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Advanced Modeling of CO2 Emissions in Gas Furnaces Using AIC and BIC Methods

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Transfer function model. Y(t) =  + u a(t-u) X(u) + (t) dZY() = A() dZX() + dZ( ) Y(t) =  exp{it}dZY() Gas furnace data - Box & Jenkins Sampling interval 9 sec., observations for 296 pairs of data points X: 0.60 of input - 0.04 (input gas rate in cuft/min) Y: % CO2 in outlet gas

Time side inference. Comparing alternate models. Supose there are J AICj = -2 log L(Ĝj ) + 2 sj sj is the number of paremeters in Gj There is also BICj = -2 log L(Ĝj ) + sj log nj Fundamental contributions of statistics