计算机科学
能源消耗
时间序列
系列(地层学)
人工智能
深度学习
能量(信号处理)
机器学习
统计
数学
电气工程
工程类
地质学
古生物学
作者
Nédra Mellouli,Mahdjouba Akerma,Minh Tu Hoang,Denis Leducq,Anthony Delahaye
标识
DOI:10.1007/978-3-030-28374-2_12
摘要
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 in 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. Four deep neural network architectures derived from the LSTM architecture were studied, adapted and compared. Their validation was carried out using experimental data collected in a cold room in order to assess their performance in predicting demand response.
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