This projects describes a new kind of thermostat, where the setpoint is not necessarily an array of times and temperatures. Rather, the setpoint is a cost, and the output of the thermostat program is the expected setpoints to achieve this cost over the next month, based on typical weather patterns. The approach is possible given the innovative system model effort, which models the home in such a way as to account for Gaussian disturbances in weather and model error, resulting in cumulative costs that are within a certain bound of the system?s prediction. For more than a decade, the promise of smart-thermostats has been to lower energy costs?but to date, the exact amount that energy costs can be lowered is non-trivial to calculate, or even predict. This work allows homeowners to equate a setpoint schedule with cost of anticipated energy usage, and is shown (in previous publications) to predict that anticipated energy usage within 10% error. Further, if a homeowner sets a desired spending setpoint (e.g., $100 for this month), the setpoint schedule can be generated and (using data gathered previously from that home) can be bounded by certain error amounts that are derived from predicted weather deviations.
Home energy made up nearly 25% of all energy consumed in the US in 2010, and 31% of this energy is estimated to come from home heating, ventilation, and air conditioning (HVAC) systems. Yet, the inputs available to a homeowner for those systems are not easily correlated to the energy consequences, making it difficult (if not impossible) for a homeowner to choose setpoints in order to meet a budget each month. The proposed work will produce technology that can impact this very large percentage of the energy market in the US (and abroad), with little or no necessary construction changes required to the home. Using this technology, current manufacturers can use data they are already collecting to put the consumer in control or a large portion of their home energy use.
This project investigated the commercial possibilities of a new kind of home thermostat that helps users to visualize and control how much they spend on their heating and cooling. Previous work indicated the technical feasibility of the technology. This project focuses on examining the potential market and other business-side considerations. 1. Intellectual Merit Studies have shown that while programmable thermostats have the potential for 10 – 30% energy savings, actual savings often fall short of these values. In addition to problems programming the thermostat itself, users are tasked with determining the heating and cooling schedule. From a consumer perspective, energy consumption is invisible and abstract, leading to a poor understanding of energy consumption and energy conservation. The proposed technology alleviates these problems by explicitly correlating cost and setpoint. Based on data previously gathered from the home and the predicted weather, the technology is able to predict future usage. Users are provided with a monthly estimate of HVAC cost. As the user changes the set point schedule, the technology is able to update the predicted cost. Alternatively, if a homeowner sets a desired spending constraint (e.g., $100 for this month), a recommender system is generates a custom setpoint schedule, based on data gathered from the home, to balance comfort and cost. 2. Broader Impacts Home energy made up nearly 25% of all energy consumed in the US in 2010, and 31% of this energy is estimated to come from home heating, ventilation, and air conditioning (HVAC) systems. Yet, the inputs available to a homeowner for those systems are not easily correlated to the energy consequences. Thus it is difficult (if not impossible) for a homeowner to choose setpoints in order to meet a budget each month. The proposed work will produce licensable technology that can impact this very large percentage of the energy market in the US (and abroad), with little or no necessary construction changes required to the home. Using this licensable technology, existing thermostat manufacturers can use data they are already collecting to put the consumer in control or a large portion of their home energy use. 3. Project Outcomes The team participated in the NSF I-Corps program, as a result of those interactions, several key results followed: interviews with over 100 potential customers refinement of the value propositions determination of the minimum viable product and a refocusing on how that product will be introduced to the market determination of the target markets and potential partnerships founding of a new company, Acomni, LLC creation of a marketing prototype for the fundamental technology At the conclusion of the I-Corps program, held in Ann Arbor, MI in summer 2012, our team was selected as "Best Team". 4. Continuing Efforts Following the close of the I-Corps program, we have continued our work to pursue commercialization of the proposed technology. The project team has been involved in a number of activities: demonstration at the ACM Workshop on Embedded Systems For Energy-Efficiency In Buildings (BuildSys) poster at the Second Annual UA/ASU Student Conference on Renewable Energy Science, Technology, and Policy (AzSEC), Distinguished Poster Award pitched the technology at Idea Funding participation in Startup Tucson working with the University to submit paperwork for copyright, provisional patent, and enabling disclosures submission of 3 separate funding proposals, including an NSF SBIR Phase I working with VentureAccel in Tucson, AZ which servers as an accelerator for high-tech start-ups
