Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption
| Autor: | Anthony Delahaye, Minh Hoang, Denis Leducq, Nedra Mellouli, Mahdjouba Akerma |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique Avancée de Saint-Denis (LIASD), Université Paris 8 Vincennes-Saint-Denis (UP8), Génie des procédés frigorifiques pour la sécurité alimentaire et l'environnement (UR FRISE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Consumption (economics)
Multivariate statistics Mathematical optimization business.industry Computer science Deep learning Energy consumption [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Network topology 7. Clean energy Demand response [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering Artificial intelligence Time series business Smoothing ComputingMilieux_MISCELLANEOUS |
| Zdroj: | 11th International Conference on Knowledge Discovery and Information Retrieval 11th International Conference on Knowledge Discovery and Information Retrieval, Sep 2019, Vienna, France. pp.384-391, ⟨10.5220/0008168203840391⟩ KDIR |
| DOI: | 10.5220/0008168203840391⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|