Temperature Forecasts with Stable Accuracy in a Smart Home

Abstract 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.