Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption

Nédra Mellouli, Mahdjouba Akerma, Minh Hoang, Denis Leducq, Anthony Delahaye

2019

Abstract

We propose to study the dynamic behavior of indoor temperature and energy consumption in a cold room during demand response periods. Demand response is a method that consists of smoothing demand over time, seeking to reduce or even stop consumption during periods of high demand in order to shift it to periods of lower demand. Such a system can therefore be tackled as the study of a time-series, where each behavioral parameter is a time-varying parameter. Different network topologies are considered, as well as existing approaches for solving multi-step ahead prediction problems. The predictive performance of short-term predictors is also examined with regard to prediction horizon. The performance of the predictors are evaluated using measured data from real scale buildings, showing promising results for the development of accurate prediction tools.

Paper Citation

in Harvard Style

Mellouli N., Akerma M., Hoang M., Leducq D. and Delahaye A. (2019). Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 384-391. DOI: 10.5220/0008168203840391

in Bibtex Style

@conference{kdir19,
author={Nédra Mellouli and Mahdjouba Akerma and Minh Hoang and Denis Leducq and Anthony Delahaye},
title={Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={384-391},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008168203840391},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption
SN - 978-989-758-382-7
AU - Mellouli N.
AU - Akerma M.
AU - Hoang M.
AU - Leducq D.
AU - Delahaye A.
PY - 2019
SP - 384
EP - 391
DO - 10.5220/0008168203840391
PB - SciTePress