A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 h ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning...

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Bibliographic Details
Main Authors: Athanasios Aris Panagopoulos, Filippos Christianos, Michail Katsigiannis, Konstantinos Mykoniatis, Marco Pritoni, Orestis P. Panagopoulos, Therese Peffer, Georgios Chalkiadakis, David E. Culler, Nick Jennings, Timothy Lipman
Format: Default Article
Published: 2020
Subjects:
energy demand
energy consumption
forecasting
smart buildings
Online Access:https://hdl.handle.net/2134/16930843.v1
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https://hdl.handle.net/2134/16930843.v1

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