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

这个项目描述了一种新的恒温器，它的设定值不一定是一组时间和温度。相反，设定值是一种成本，恒温器程序的输出是下个月根据典型天气模式实现这一成本的预期设定值。考虑到创新系统模型的努力，该方法是可能的，该模型以这样一种方式建模，以考虑天气和模型误差中的高斯干扰，导致累积成本在系统的一定范围内。年代的预测。十多年来，智能恒温器一直致力于降低能源成本。但到目前为止，能源成本可以降低的确切数量很难计算，甚至无法预测。这项工作允许业主将设定时间表与预期能源使用成本等同起来，并显示（在以前的出版物中）预测预期能源使用误差在10%以内。此外，如果房主设置了一个期望的支出设定值（例如，本月100美元），则可以生成设定值时间表，并且（使用先前从该房屋收集的数据）可以受到来自预测天气偏差的某些误差量的限制。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，房主对这些系统的可用输入并不容易与能源结果相关联，这使得房主很难（如果不是不可能的话）选择设定值以满足每月的预算。拟议的工作将产生能够影响美国（和国外）能源市场很大一部分的技术，而很少或根本不需要对家庭进行必要的建筑改变。利用这项技术，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的大部分家庭能源使用。

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.