An Integrated Framework for Building’s Energy Management based on Deep Learning
Publisher
Leykam
Source
Technologie- und Klimawandel: Energie-Gebäude-Umwelt, 465-471
Date Issued
2020-11-26
Author(s)
Meintanis, I.
Monios, N.
Livieris, I.E.
Kampourakis, M.
Fourakis, S.
Kyriakoulis, N.
Kokkorikos, S.
Chondronasios, A.
Abstract
Forecasting the building energy consumption constitutes a significant factor for a wide variety
of applications including planning, management and optimization. Nowadays, research is focused towards
the development of more efficient and sustainable energy management systems which focus on minimising
energy waste. These systems are based on intelligent models, which provide accurate predictions of future
energy demand/load, both at aggregate and individual site level. In this work, we present a holistic integrated
solution for the buildings’ energy management systems using deep learning methods. The proposed solution
is based on efficient deep-learning forecasting models for short-term local weather parameters and energy load
consumption. The developed forecasting models are integrated into the smart energy management system of
the building for taking the proper decisions to ensure efficient utilization of energy resources.
of applications including planning, management and optimization. Nowadays, research is focused towards
the development of more efficient and sustainable energy management systems which focus on minimising
energy waste. These systems are based on intelligent models, which provide accurate predictions of future
energy demand/load, both at aggregate and individual site level. In this work, we present a holistic integrated
solution for the buildings’ energy management systems using deep learning methods. The proposed solution
is based on efficient deep-learning forecasting models for short-term local weather parameters and energy load
consumption. The developed forecasting models are integrated into the smart energy management system of
the building for taking the proper decisions to ensure efficient utilization of energy resources.
Funding(s)
Type
Konferenzbeitrag
File(s)![Thumbnail Image]()
Loading...
Name
9783701104604_465.pdf
Size
2.24 MB
Format
Adobe PDF
Checksum
(MD5):8f511365c85eec1a9b711b9c3409ed24