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Activity-aware HVAC power demand forecasting

Author
Sala, E.; Delgado Prieto, M.; Kampouropoulos, K.; Romeral, L.
Type of activity
Journal article
Journal
Energy and buildings
Date of publication
2018-07-01
Volume
170
First page
15
Last page
24
DOI
https://doi.org/10.1016/j.enbuild.2018.03.087 Open in new window
Repository
http://hdl.handle.net/2117/119594 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0378778817329456 Open in new window
Abstract
The forecasting of the thermal power demand is essential to support the development of advanced strategies for the management of local resources on the consumer side, such as heating ventilation and air conditioning (HVAC) equipment in buildings. In this paper, a novel hybrid methodology is presented for the short-term load forecasting of HVAC thermal power demand in smart buildings based on a data-driven approach. The methodology implements an estimation of the building's activity in order to i...
Citation
Sala, E., Delgado Prieto, M., Kampouropoulos, K., Romeral, L. Activity-aware HVAC power demand forecasting. "Energy and buildings", 1 Juliol 2018, vol. 170, p. 15-24.
Keywords
energy management systems, load prediction, machine learning, neural networks, smart buildings
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

Participants