SIM Home

The goal of this project is to design and develop the CalPlug SIM Home, an expansion of the current CalPlug's SIM Labs, to address the modern networked home. The CalPlug SIM Labs methodology, which focuses on entertainment appliances, will serve as a launch pad and standard baseline to create the SIM Home. The SIM Home will simulate a realistic residential environment that is a convenient setting to test the energy savings potentials of emerging household appliances from different manufacturers. The SIM Home will serve as a connected ecosystem to evaluate how emerging products can be integrated and will envision the next trends of residential smart energy management. In this next phase, the SIM Home will also review prior research about which devices are used in American homes and how different users might use them, to construct a range of testing environments. The resulting networked testing results will thus supplement CalPlug's independent benchmark testing of each of the devices, providing energy use estimates for a range of real-life usage models. Serving as an objective third-party testing site for manufacturers, SIM Home will work to deliver unbiased results of energy savings and efficiency of any product subjected to the SIM Labs methodology.

The Simulation, Integration, and Management (SIM) Home project aimed to assist with several aspects of program development within SCE’s Emerging Technology group. The first objective was to build an advanced metering environment including a range of products, providing the capacity for networked plug load device testing to supplement individual energy tests. The second objective was to produce energy consumption estimates for products included in the initial development of the SIM Home, in particular noting the estimated range of consumption based on varied device use patterns. The third purpose was to develop two applications for communicating this information to users: a web-based energy display, and an animated avatar providing human-like verbal communication. We designed and constructed the SIM Home testing facility, including an automated testing platform, a web application known as the SIM Bulletin, and an additional mobile application which includes an embodied agent, Energy Monitor and Management Assistant (EMMA) as an interface. This enabled us to analyze plug load devices from the consumer’s perspective: the performance of each device was compared against the product specifications, and how potential user scenarios may cause additional wasted energy. The project approached this problem by first analyzing existing literature and survey data to better understand which devices are present in California homes, and how much (or how frequently) consumers use the devices. For device categories without sufficient data, we made educated assumptions about how the devices were used on a daily basis. The literature research results were then converted into device use schedules, forming part of the input parameters into the energy modeling software. Then, the devices were tested using high-resolution equipment to capture the actual power consumption, which was calculated to energy consumption over time, given the pattern of usage. As the final step, the energy modeling software output the annual energy consumption of the plug load devices. The simulations modeled energy usage at “light” and “heavy” use levels, to compare to the “moderate” levels typically used for energy estimates. The results from these simulations indicate that even if households contained the same plug load devices, actual household energy consumption could be substantially higher or lower than standard estimates, depending on how often consumers used their devices, and whether they employed power management settings. This has important implications for standard annual energy consumption estimates, which assume “average” households with “average” usage behaviors. Specifically, the moderate or standard usage profile was set at the median amount of time the device was used, according to survey data, with default power management settings (if any) employed. By comparison, if the household actually exhibited light use – defined as 10th percentile of duration and frequency of use and aggressive power management settings – the moderate estimate would overestimate consumption by 236%. If the household actually exhibited heavy use – defined as 90th percentile of duration and frequency of use and disabled power management settings – the moderate estimate would underestimate consumption by 61%. Comparing the heavy-use household to the light-use household indicated that some households may consume over 700% more than others. Yet this is certainly an underestimate of the extent of the problem, as SIM Home simulates a one-bedroom household that contains only fifteen plug-load devices. Most one-bedroom households would have many more devices, and of course larger homes would have even more; with every device added to the estimate, the range between the lowest-use household and the highest-use household can be expected to grow. That is: the extent to which any “average” estimate of energy usage is wrong would get larger and larger. Based on our results, we recommend the following: -To reduce the gap between estimated energy consumption and actual energy consumption, more time and effort is needed to further investigate the potential errors in plug load energy simulations, including collecting and utilizing better data on how devices are used across a wide range of real-life households. -Since plug load devices and white goods produce heat during operation, the heat generated can cause an HVAC system create a larger energy footprint. Therefore, further investigation is also needed in this area. -Some plug load testing procedures should also consider energy consumption, in addition to the current practice of testing only power consumption. For example, one device may show lower power consumption in the “on” state than a second one, yet take longer in that state to complete the tasks users are likely to require of it, possibly leading to the same (or higher) energy consumption than the second device. Or, one device may use less power in the “idle” state, but spend more time idling before transitioning to standby or sleep mode. -When designing plug load devices that are typically connected to related devices, such as entertainment systems, energy-saving features should be designed from a system approach, instead of simply relying on individual devices’ power management features.