Consistently accurate forecasts of temperature within buildings from sensor data using ridge and lasso regression
Consistently accurate forecasts of temperature within buildings from sensor data using ridge and lasso regression
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DOI：
10.1016/j.future.2018.02.035
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发表时间：
2020-09-01
影响因子：
7.5
通讯作者：
Alfandi, Omar
中科院分区：
文献类型：
--
作者：
Al-Obeidat, Feras;Spencer, Bruce;Alfandi, Omar
在所有产生的能量中，有很大一部分用于为建筑物供暖和降温。其中一些能源可以通过使用温度控制器来节省，该控制器可以访问建筑物内部温度的准确预测。这些预测依赖于从传感器收集的信息，包括温度、湿度、日照以及烹饪和洗衣设备的电力负荷。使用来自两个带有各种传感器的家庭的公开数据，我们通过将其建模为最新传感器值的线性函数来预测室内温度。这些模型是使用改进了标准最小二乘回归的技术建立的：使用交叉验证的正向逐步回归、脊回归和套索回归。通过套索回归，我们准确地预测了两个房子在1.8摄氏度以内的未来48小时内每一刻钟的内部温度。我们还预测了未来两天每一刻钟的气温变化，在0.05摄氏度以内，这比之前使用相同数据预测的气温变化有很大改进。我们提出了一种预测即服务的商业模式，其中保证一致的准确性对于吸引客户和节约能源非常重要。(C)2018爱思唯尔B.V.保留所有权利。
A significant portion of all energy generated is used to heat and cool buildings. Some of that energy can be saved by using a temperature controller with access to an accurate forecast of a building's internal temperature. These forecasts depend on information gathered from sensors, including temperature, humidity, sunlight, and the electrical load of cooking and laundry appliances. Using publicly available data from two homes with a wide variety of sensors, we forecast internal temperature by modelling it as a linear function of recent sensor values. These models are built using techniques that improve upon standard least squares regression: forward stepwise, ridge and lasso regressions, using cross-validation. With lasso regression, we accurately forecast internal temperature every quarter hour over the next 48 h within 1.8 degrees C in both houses. We also forecast temperature changes over each quarter-hour for the next two days, within 0.05 degrees C, which significantly improves on previous forecasts of temperature changes using the same data. We propose a business model for forecasting as a service, where guarantees of consistent accuracy are important for attracting clients and saving energy. (C) 2018 Elsevier B.V. All rights reserved.