Carbon-Neutral ESG Method Based on PV Energy Generation Prediction Model in Buildings for EV Charging Platform
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Published:2023-08-18
Issue:8
Volume:13
Page:2098
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ISSN:2075-5309
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Container-title:Buildings
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language:en
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Short-container-title:Buildings
Author:
Yoon Guwon1ORCID, Kim Seunghwan2, Shin Haneul2, Cho Keonhee2, Jang Hyeonwoo1, Lee Tacklim2, Choi Myeong-in2ORCID, Kang Byeongkwan2ORCID, Park Sangmin2ORCID, Lee Sanghoon2ORCID, Park Junhyun2, Jung Hyeyoon2, Shmilovitz Doron3, Park Sehyun12
Affiliation:
1. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea 2. Department of Intelligent Energy and Industry, Chung-Ang University, Seoul 06974, Republic of Korea 3. School of Electrical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
Abstract
Energy prediction models and platforms are being developed to achieve carbon-neutral ESG, transition buildings to renewable energy, and supply sustainable energy to EV charging infrastructure. Despite numerous studies on machine learning (ML)-based prediction models for photovoltaic (PV) energy, integrating models with carbon emission analysis and an electric vehicle (EV) charging platform remains challenging. To overcome this, we propose a building-specific long short-term memory (LSTM) prediction model for PV energy supply. This model simulates the integration of EV charging platforms and offer solutions for carbon reduction. Integrating a PV energy prediction model within buildings and EV charging platforms using ICT is crucial to achieve renewable energy transition and carbon neutrality. The ML model uses data from various perspectives to derive operational strategies for energy supply to the grid. Additionally, simulations explore the integration of PV-EV charging infrastructure, EV charging control based on energy, and mechanisms for sharing energy, promoting eco-friendly charging. By comparing carbon emissions from fossil-fuel-based sources with PV energy sources, we analyze the reduction in carbon emission effects, providing a comprehensive understanding of carbon reduction and energy transition through energy prediction. In the future, we aim to secure economic viability in the building energy infrastructure market and establish a carbon-neutral city by providing a stable energy supply to buildings and EV charging infrastructure. Through ongoing research on specialized models tailored to the unique characteristics of energy domains within buildings, we aim to contribute to the resolution of inter-regional energy supply challenges and the achievement of carbon reduction.
Funder
Korea Government Ministry of Trade, Industry and Energy Chung-Ang University
Subject
Building and Construction,Civil and Structural Engineering,Architecture
Reference57 articles.
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