The Model—Simulating Usage and Retrofits

In selecting the proper suite of retrofits, the researchers' aim was to use an energy simulation tool to create a model, verify the model by matching the results with real-time energy usage data before the retrofit, perform an optimization analysis on the home to inform the retrofit actions required, and then use the model to predict the energy consumption post retrofit. The first step was to establish a baseline of the home's energy profile, including measurements of the structure and how much energy it currently consumes—information required to determine the best way to upgrade the home, whether it be window replacement, insulation, the addition of solar equipment, or other measures. After the house was taken over in July 2013, the baseline measurement system was installed and operational in November.

In the recent past, scores of increasingly sophisticated software solutions have been developed to provide energy modeling of residential buildings. Researchers at the ReNEWW House chose the software known as BEOpt (Building Energy Optimization), developed by the National Renewable Energy Laboratory (NREL), in support of the DOE Building America program goal to develop market-ready energy solutions for new and existing homes. BEOpt uses existing, established simulation engines (currently DOE-2.2 and EnergyPlus), and is able to run optimization analyses and recommend the most cost-effective improvements that can be applied. The software produces detailed simulation-based analysis and design optimization predicated on such house features as size, occupancy, age, location, and other factors. The objective of the optimization analysis is to minimize the annualized energy-related cost over a 30-year analysis period. The annualized energy-related cost (AERC) measure accounts for four major household cash flows which are loan costs (principal + interest) for performing the retrofit work, utility bills, replacement costs for when equipment such as a water heater wears out within the analysis period, and residual value of all depreciable equipment in the house at year 30 of the analysis period.

Energy modeling software is widely used to ascertain a home's energy performance, determine retrofit effectiveness, and size HVAC systems. Many experts agree that energy modeling is a solid investment and leads to good decision making. However, residential energy modeling, particularly of older homes, is not without drawbacks, and researchers at the ReNEWW House did acknowledge the limitations of the modeling process. This type of modeling is a complex process, with many required inputs that are often difficult to measure. Further complicating the process, each dwelling is different, increasing the difficulty of using standardized measures, and there is a wide discrepancy in retrofit costs among various markets and time periods. In addition, a key driver of energy costs is occupant behavior, which is extremely challenging to quantify. Even within a family, there can be a significant difference as to how a particular occupant sets comfort criteria, uses lighting, and regulates heating or cooling systems. Energy philosophies of occupants may differ widely, ranging from avid energy savers to ordinary consumers to those who are even wasteful or oblivious to energy usage. Occupant behavior in residential energy modeling, including BEOpt, typically follows the Building America Simulation Protocol which dictates usage patterns such as length of showers, temperature set points, appliance usage, etc. As a result, researchers did not use the model to dictate the outcome; rather, the energy models were used to inform decisions which were ultimately finalized by leveraging intuition. The model was not intended to be a pure retrofit case study.

In developing the baseline, researchers created a 3D model and selected the inputs that closely matched the dwelling's structural characteristics. Inputs that were selected were related to the geometry of the home, the envelope characteristics, the HVAC system, and any other device that uses energy, such as lighting fixtures and appliances.

There are four main factors that affect the energy consumption of a household: the building envelope itself; the HVAC system and hot water heater (collectively the mechanicals of the house); the end use devices, such as lighting and appliances; and human behavior. Inputs for the first three factors were relatively easy to select and represented fairly accurate parameters of the real condition of the existing home. Such inputs can be selected from a large library of predefined options embedded in the software. The fourth factor is human behavior, such as length of a shower, or appliance usage. Because these factors are so difficult to define, the software simulates human behavior from generally accepted assumptions based on NREL studies that sought to describe the average American family energy consumption. As previously mentioned, these behavioral assumptions are documented in the Building America Simulation Protocol. The table on page 154 highlights the main parameters chosen to simulate the pre-retrofit conditions of the test house.

As the ReNEWW house is located in a cold climate zone, understandably, heating demand represented the largest use of energy, with the baseline model predicting 70 percent of energy consumption was due to heating. The effects of the cold climate were exacerbated by the dwelling's lack of insulation and poorly sealed envelope compared to an average home in Illinois. Because recent data related to Indiana were not easily accessible, baseline model data was compared to a similar geographic and climate zone in the neighboring state of Illinois. The most noticeable difference between the two is the dissimilarity between the percentages of energy consumption due to the heating—72 percent for the test house and 51 percent for an average home in Illinois. This analysis showed early on that most of the energy savings would come from focusing on the building envelope. It is important to note, however, that in newer homes which typically have better building envelopes, space conditioning is becoming a less dominant factor in relative energy use; more attention might be better directed in a retrofit to other aspects of energy consumption, such as appliances, plug loads, and lighting.