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T1  - Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation 
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UR  - http://hdl.handle.net/10138/334553 
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A1  - Nabavi, Seyed Azad; Hossein Motlagh, Naser; Zaidan, Martha Arbayani; Aslani, Alireza; Zakeri, Behnam 
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PB  -  
Y1  - 2021 
LA  - eng 
AB  - Buildings are responsible for 33% of final energy consumption, and 40% of direct and indirect CO2 emissions globally. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding to this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy dema... 
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KW  - 113 Computer and information sciences; Buildings; Renewable energy sources; Forecasting; Energy consumption; Energy management; Discrete wavelet transforms; Deep learning; Smart active buildings; AI-based energy model; deep learning; LSTM; energy system modeling; building energy management; discrete wavelet transformation; energy supply scheduling; SHORT-TERM LOAD; MANAGEMENT STRATEGY; NEURAL-NETWORK; POWER; DEMAND; SYSTEM; CONSUMPTION; PREDICTION; INTEGRATION 
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