Accurately forecasting temperatures in smart buildings using fewer sensors

ORCID Identifiers

0000-0003-1093-4870

Document Type

Article

Source of Publication

Personal and Ubiquitous Computing

Publication Date

11-1-2019

Abstract

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Forecasts of temperature in a “smart” building, i.e. one that is outfitted with sensors, are computed from data gathered by these sensors. Model predictive controllers can use accurate temperature forecasts to save energy by optimally using heating, ventilation and air conditioners while achieving comfort. We report on experiments from such a house. We select different sets of sensors, build a temperature model from each set, and compare the accuracy of these models. While a primary goal of this research area is to reduce energy consumption, in this paper, besides the cost of energy, we consider the cost of data collection and management. Our approach informs the selection of an optimal set of sensors for any model predictive controller to reduce overall costs, using any forecasting methodology. We use lasso regression with lagged observations, which compares favourably to previous methods using the same data.

ISSN

1617-4909

Publisher

Springer London

Volume

23

Issue

5-6

First Page

921

Last Page

929

Disciplines

Electrical and Computer Engineering

Keywords

Energy efficiency, Feature selection, Internet of things, Model predictive control, Sensor networks, Temperature forecast

Scopus ID

85038082850

Indexed in Scopus

yes

Open Access

no

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