• Energy Research

The National Renewable Energy Laboratory (NREL) conducted a series of assessments of the U.S. Department of Energy's (DOE) proposed Home Energy Scoring Tool (HEST). This report is an assessment of the 4/27/2012 release of HEST. Predictions of electric and natural gas consumption were compared with weather-normalized utility billing data for a mixture of newer and older homes located in Oregon, Wisconsin, Minnesota, North Carolina and Texas.

The National Renewable Energy Laboratory (NREL) conducted a series of assessments of the U.S. Department of Energy's (DOE) proposed Home Energy Scoring Tool (HEST). This report is an assessment of the 4/27/2012 release of HEST. Predictions of electric and natural gas consumption were compared with weather-normalized utility billing data for a mixture of newer and older homes located in Oregon, Wisconsin, Minnesota, North Carolina and Texas.

Abstract This paper details the process and results from the first step of a three-step research process. This first step looks to identify the most predictive pre-retrofit metric of energy consumption to utilize in a model to predict the energy savings post retrofit. The ultimate goal of this research is to predict candidacy for retrofit using only a combination of demographic and home-characteristics data that is available for the entirety of the U.S. residential housing stock. This is important, as utility data is almost always protected for privacy and thus unavailable to assist in targeting where energy efficiency retrofits will be successful. It is found that the best metric is the simplest, total energy consumption divided by total floor area. In addition to evaluating which pre-use metric is most indicative of post retrofit savings, the paper evaluates the endogenous component of pre-use to post use and a potential method to alleviate this endogeneity. The research finds that by removing the year that is used to calculate the savings as the baseline pre-use year removes a portion of the endogeneity. It is also found that one year before the savings base year is the best year to utilize as the base.

Abstract This paper details the process and results from the first step of a three-step research process. This first step looks to identify the most predictive pre-retrofit metric of energy consumption to utilize in a model to predict the energy savings post retrofit. The ultimate goal of this research is to predict candidacy for retrofit using only a combination of demographic and home-characteristics data that is available for the entirety of the U.S. residential housing stock. This is important, as utility data is almost always protected for privacy and thus unavailable to assist in targeting where energy efficiency retrofits will be successful. It is found that the best metric is the simplest, total energy consumption divided by total floor area. In addition to evaluating which pre-use metric is most indicative of post retrofit savings, the paper evaluates the endogenous component of pre-use to post use and a potential method to alleviate this endogeneity. The research finds that by removing the year that is used to calculate the savings as the baseline pre-use year removes a portion of the endogeneity. It is also found that one year before the savings base year is the best year to utilize as the base.

Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.

Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.

Shifting and shedding power demand in buildings can be cost-effective techniques for grids to function reliably and for end users to earn compensation. Grid operators reimburse customers in proportion to the quantity of load shed. Simple data-driven methods are used to quantify this shed, which is the difference between a measured load during the event and modeled “baseline” that would have occurred in absence of the event. These methods have evolved over the years and in many cases have been integrated with building physics, to make them a hybrid between physics based and empirical models. However, there is no comprehensive analysis that provides guidance to building operators, grid operators and researchers in selecting appropriate models based on their specific needs and available data. This work aims to fill this gap by critically assessing the performance of baseline models put forward from the year 2000 through 2023. The literature reviewed includes reports generated by grid operators, reports from national laboratories and academic journal articles. The work outlines modeling features like the inputs, training period, estimation method, adjustments to fine tune the predictions and metrics to evaluate the performance. A comprehensive list of 50 models has been provided. For each model, the study explores the applicability of the model to weather sensitive buildings, variability in the building profile, timing of the event, and whether the building reduces energy consumption before an event. The work identifies the situations in which a particular model works and draws lessons based on evidence of performance. Finally, recommendations to aid in model selection are given.