Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

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http://hdl.handle.net/10138/334553

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Nabavi , S A , Hossein Motlagh , N , Zaidan , M A , Aslani , A & Zakeri , B 2021 , ' Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation ' , IEEE Access , vol. 9 , pp. 125439-125461 . https://doi.org/10.1109/ACCESS.2021.3110960

Title: Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation
Author: Nabavi, Seyed Azad; Hossein Motlagh, Naser; Zaidan, Martha Arbayani; Aslani, Alireza; Zakeri, Behnam
Contributor organization: Department of Computer Science
Helsinki Institute of Sustainability Science (HELSUS)
Institute for Atmospheric and Earth System Research (INAR)
Global Atmosphere-Earth surface feedbacks
Date: 2021
Language: eng
Number of pages: 23
Belongs to series: IEEE Access
ISSN: 2169-3536
DOI: https://doi.org/10.1109/ACCESS.2021.3110960
URI: http://hdl.handle.net/10138/334553
Abstract: 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 demand and supply for buildings. This method analyzes several factors including electricity price, uncertainty in climatic factors, availability of renewable energy sources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management to minimize the reliance on the grid and electricity cost, especially during the peak hours. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid by 84%, which offers electricity cost savings by 87%. Finally, two smart active buildings configurations are financially analyzed for the next thirty years. Based on the results, the proposed smart building with solar Photo-Voltaic (PV), wind turbine, inverter, and 40.5 kWh energy storage has a financial breakeven point after 9 years with wind turbine and 8 years without it. This implies that implementing wind turbines in the proposed building is not financially beneficial.
Subject: 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
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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