Title
Temperature Forecasts with Stable Accuracy in a Smart Home
Source of Publication
Procedia Computer Science
Abstract
© 2016 The Authors. We forecast internal temperature in a home with sensors, modeled as a linear function of recent sensor values. When delivering forecasts as a service, two desirable properties are that forecasts have stable accuracy over a variety of forecast horizons - so service levels can be predicted - and that the forecasts rely on a modest amount of sensor history - so forecasting can be restarted soon after any data outage due to, for example, sensor failure. From a publicly available data set, we show that sensor values over the past one or two hours are sufficient to meet these demands. A standard machine learning method based on forward stepwise linear regression with cross validation gives forecasts whose out-of-sample errors increase slowly as the forecast horizon increases, and that are accurate to within one fifth of a degree C over three hours, and to within about one half degree C over six hours, based on one or two hours of history. Previous results from this data achieved errors within one degree C over three hours based on five days of history.
Document Type
Conference Proceeding
First Page
726
Last Page
733
Publication Date
1-1-2016
DOI
10.1016/j.procs.2016.04.160
Recommended Citation
Spencer, Bruce and Al-Obeidat, Feras, "Temperature Forecasts with Stable Accuracy in a Smart Home" (2016). Scopus Indexed Articles. 1613.
https://zuscholars.zu.ac.ae/scopus-indexed-articles/1613