Document Type

Conference Proceeding

Source of Publication

Procedia Computer Science

Publication Date

1-1-2016

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.

ISSN

1877-0509

Publisher

Elsevier

Volume

83

First Page

726

Last Page

733

Disciplines

Computer Sciences

Keywords

domotic house, forecast accuracy, forward stepwise linear regression, service level agreement, Temperature forecasts

Scopus ID

84971219282

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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