I-Corps: A Cost Limited Home Thermostat (CLD/HT)

该项目描述了一种新的恒温器，其设置点不一定是一组时间和温度。相反，设定点是一种成本，根据典型的天气模式，恒温器程序的输出是在下个月实现这一成本的预期设定点。S预测，考虑到创新的系统模型工作，这种方法是可能的，它以一种方式对房屋进行建模，以解决天气中的高斯干扰和模型误差，导致累计成本在系统的一定范围内。十多年来，智能温控器的承诺一直是降低能源成本--但到目前为止，能源成本到底能降低多少，很难计算，甚至很难预测。这项工作允许房主将设定点时间表等同于预期能源使用的成本，并且(在以前的出版物中)预测预期能源使用的误差在10%以内。此外，如果房主设置了期望的支出设置点(例如，本月的100美元)，则可以生成设置点时间表，并且(使用先前从该房屋收集的数据)可以由从预测的天气偏差得出的某些误差量来限定。2010年，家庭能源占美国所有能源消耗的近25%，其中31%的能源估计来自家庭供暖、通风和空调(HVAC)系统。然而，房主可用于这些系统的投入并不容易与能源后果相关，这使得房主很难(如果不是不可能)选择设定点来满足每月的预算。拟议中的工作将产生技术，可以影响美国(和海外)能源市场的这一非常大的比例，而不需要对住宅进行必要的建筑改造。使用这项技术，当前的制造商可以使用他们已经收集的数据来控制消费者或他们家庭能源使用的很大一部分。

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.