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Forecasting indoor temperatures during heatwaves: Do more complex models provide better predictions?

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conference contribution
posted on 21.06.2021, 11:29 by Matej Gustin, Rob McLeod, Kevin LomasKevin Lomas
A novel application of semi-parametric Generalized Additive Models (GAMs) was developed to forecast elevated indoor temperatures. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that GAMs are capable of slightly improving the forecasting accuracy, but only at short horizons (3-6 hours ahead).

Funding

EPSRC Centre for Doctoral Training in Energy Demand (LoLo)

Engineering and Physical Sciences Research Council

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LEEDR: Low Effort Energy Demand Reduction (Part 2 of the Call)

Engineering and Physical Sciences Research Council

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History

School

  • Architecture, Building and Civil Engineering

Published in

Proceedings of Building Simulation 2019: 16th Conference of IBPSA

Pages

4243 - 4250

Source

16th IBPSA International Conference and Exhibition

Publisher

International Building Performance Simulation Association (IBPSA)

Version

VoR (Version of Record)

Rights holder

© International Building Performance Simulation Association

Acceptance date

01/04/2019

Copyright date

2020

ISBN

9781775052012

ISSN

2522-2708

Book series

Building Simulation Conference Proceedings; 6

Language

en

Editor(s)

V. Corrado; E. Fabrizio; A. Gasparella; F. Patuzzi

Location

Rome, Italy

Event dates

2nd September 2019 - 4th September 2019

Depositor

Prof Kevin Lomas. Deposit date: 20 June 2021

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