Targeting Residential Energy Assistance

Abstract

This paper describes a four-step method to analyze the utility bills and weather data from multiple residences to target buildings for specific energy conservation retrofits. The method is also useful for focusing energy assessments on the most promising opportunities. The first step of the method is to create a three-parameter changepoint regression model of energy use versus weather for each building and fuel type. The three model parameters represent weather independent energy use, the building heating or cooling coefficient and the building balance-point temperature. The second step is to drive the models using typical TMY2 weather data to determine Normalized Annual Consumption (NAC) for each fuel type. The third step is to create a sliding NAC with each set of 12 sequential months of utility data. The final step is to benchmark the NACs and coefficients of multiple buildings to identify average, best and worst energy performers, and how the performance of each building has changed over time. The method identifies billing errors, normalizes energy use for changing weather, prioritizes sites for specific energy-efficiency retrofits and tracks weather-normalized changes in energy use. The principle differences between this method and previously defined ones are that this method seeks to use inverse modeling proactively to identify energy saving opportunities rather than retroactively to measure energy savings, it tracks changes in building performance using sliding analysis, and it uses comparisons between multiple buildings to extract additional information. This paper describes the method, then demonstrates the method through a case study of about 300 low-income residences. After applying the method, targeted buildings were visited to determine the accuracy of the method at identifying energy efficiency opportunities. The case study shows that over 80% of the targeted buildings presented at least one of the expected problems from each type of retrofit.

Prioritizing investment in residential energy efficiency and renewable energy—A case study for the U.S. Midwest

“…The next step in the process was to normalize electricity use data for each residence by dividing by the square footage. Both the natural gas and electricity consumption over the noted time periods of each data set were analyzed using Energy Explorer software (Raffio et al, 2007), which allows a weather normalization of the energy consumption. In Figs.…”

Estimating residential energy consumption in metropolitan areas: A microsimulation approach

“…The statistical models relate unique contributions of various factors to residential energy consumption. Raffio, Isambert, Mertz, Schreier, & Kissock (2007) linearly associated housing units, socio-economic, and demographic features of households and weather data with residential energy profile. Fung, Aydinalp, & Ugursal (1999) showed that residential energy consumption is linearly correlated with energy price, household demographics, appliance features, and weather, across different end-uses.…”

Change point and degree day baseline regression models in industrial facilities

“…Most regression models have been found to be linear and of the first order due to the thermodynamic principles of energy flow in buildings and the simplicity of the statistics involving the formulation of the linear models [6]. These types of models have been commonly used in both commercial and residential analysis [7][8][9][10].…”

“…The objective of this investigation is to examine the effectiveness of simple inverse linear statistical regression models for establishing baseline energy consumption models for industrial manufacturing facilities, and to use these models to disaggregate energy consumption into dependence on production, weather, or independence from both. Extensive research using regression analysis or calibrated building simulations has been performed in order to create baseline energy consumption models for residential buildings and commercial institutions [2,[7][8][9][10][11][12][13]. However, few attempts have been made to discuss the applicability of these methodologies to generate baseline energy consumption models for industrial manufacturing facilities [14][15][16].…”

“…Understanding energy usage and increasing end-use energy efficiency of existing residential, commercial and industrial facilities is one of the important solutions to the problem of rapidly-growing worldwide energy demand. While both residential and commercial buildings have been extensively analyzed [1][2][3][4][5][6][7][8][9][10][11][12][13], industrial buildings have enjoyed minimal investigations [14][15][16]. Industrial facilities account for 33% of the annual energy usage within the United States [17], and increasing industrial buildings' energy efficiency by at least 20% over the next 10 years is the primary goal of the US Department of Energy's "Better Buildings, Better Plants Program" [18].…”