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

该项目描述了一种新型的恒温器，其中设定点不一定是时间和温度的数组。相反，设定点是成本，并且恒温器程序的输出是基于典型天气模式在下个月实现该成本的预期设定点。这种方法是可能的，因为创新的系统模型的努力，它的模式，以这样一种方式，以考虑高斯干扰的天气和模型误差，在累积成本是在一定范围内的系统？的预测。十多年来，智能恒温器的承诺一直是降低能源成本？但是到目前为止，能源成本可以降低的确切数量是不可计算的，甚至是不可预测的。这项工作允许业主等同于一个设定点时间表与预期的能源使用成本，并显示（在以前的出版物）预测，预期的能源使用在10%的误差。此外，如果房主设置期望的花费设定点（例如，这个月$100），可以生成设定点计划，并且（使用先前从该家庭收集的数据）可以由从预测的天气偏差导出的某些误差量来限制。家庭能量占2010年美国消耗的所有能量的近25%，并且估计该能量的31%来自家庭加热、通风和空调（HVAC）系统。然而，对于那些系统，房主可用的输入不容易与能量后果相关，使得房主难以（如果不是不可能的话）选择设定点以满足每个月的预算。拟议的工作将产生技术，可以影响美国（和国外）能源市场的这一非常大的比例，而很少或根本不需要对家庭进行必要的建筑改造。使用这项技术，目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.