- ABSTRACT
- 1. 서론
- 2. 인공지능 기술의 개념과 이론
- 3. 연구 동향 분석
- 4. 결론
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https://www.riss.kr/link?id=A107181822
2020
Korean
610
KCI등재
학술저널
169-176(8쪽)
1
0
상세조회0
다운로드목차 (Table of Contents)
참고문헌 (Reference)
1 강인성, "최근 건축분야의 인공지능 기계학습 연구동향 - 국내ㆍ외 연구논문을 중심으로 -" 대한건축학회 33 (33): 63-83, 2017
2 한전 KDN, "인공지능 기술과 에너지 산업 혁신"
3 조수연, "건물데이터를 통한 건물에너지 절감 가능성에 대한 연구 : 도시단위의 거시적 분석부터 미시적 건물에너지 분석사례" 대한설비공학회 29 (29): 580-591, 2017
4 UN, "World Urbanization Prospects: The 2018 Revision"
5 T.S. Dutta, "What Is The Difference Between AI, ML And Deep Learning?"
6 A. Mittal, "Understanding RNN and LSTM"
7 C. Olah, "Understanding LSTM Networks"
8 E. Terrenoire, "The contribution of carbon dioxide emissions from the aviation sector to future climate change" 14 (14): 084019-, 2019
9 A.L. Samuel, "Some Studies in Machine Learning Using the Game of Checkers" 44 : 206-226, 1959
10 A. Heidari, "Short-term energy use prediction of solarassisted water heating system: Application case of combined attention-based LSTM and time-series decomposition" 207 : 626-639, 2020
1 강인성, "최근 건축분야의 인공지능 기계학습 연구동향 - 국내ㆍ외 연구논문을 중심으로 -" 대한건축학회 33 (33): 63-83, 2017
2 한전 KDN, "인공지능 기술과 에너지 산업 혁신"
3 조수연, "건물데이터를 통한 건물에너지 절감 가능성에 대한 연구 : 도시단위의 거시적 분석부터 미시적 건물에너지 분석사례" 대한설비공학회 29 (29): 580-591, 2017
4 UN, "World Urbanization Prospects: The 2018 Revision"
5 T.S. Dutta, "What Is The Difference Between AI, ML And Deep Learning?"
6 A. Mittal, "Understanding RNN and LSTM"
7 C. Olah, "Understanding LSTM Networks"
8 E. Terrenoire, "The contribution of carbon dioxide emissions from the aviation sector to future climate change" 14 (14): 084019-, 2019
9 A.L. Samuel, "Some Studies in Machine Learning Using the Game of Checkers" 44 : 206-226, 1959
10 A. Heidari, "Short-term energy use prediction of solarassisted water heating system: Application case of combined attention-based LSTM and time-series decomposition" 207 : 626-639, 2020
11 B. Qolomany, "Role of deep LSTM neural networks and Wi-Fi networks in support of occupancy prediction in smart buildings" 50-57, 2017
12 M. Venkatachalam, "Recurrent Neural Networks"
13 K. Weng, "RNN-based forecasting of indoor temperature in a naturally ventilated residential building" IBPSA 2019 : 2019
14 H. Gaballa, "Prediction of hourly solar radiation using temperature and humidity for real-time building energy simulation" 1343 (1343): 012049-, 2019
15 T.Y. Kim, "Predicting residential energy consumption using CNN-LSTM neural networks" 182 : 72-81, 2019
16 S. Bouktif, "Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting" 13 (13): 391-, 2020
17 S. Hochreiter, "Long Short Term Memory" 9 (9): 1735-1780, 1997
18 L. Wen, "Load demand forecasting of residential buildings using a deep learning model" 179 : 106073-, 2020
19 F. Mtibaa, "LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings" 1-17, 2020
20 J. Loy-Benitez, "Imputing missing indoor air quality data via variational convolutional autoencoders: Implications for ventilation management of subway metro systems" 182 : 107135-, 2020
21 C. Xu, "Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method" 148 : 128-135, 2019
22 N. Kocyigit, "Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network" 50 : 69-79, 2015
23 A. Streltsov, "Estimating residential building energy consumption using overhead imagery" 280 : 116018-, 2020
24 Q. Huang, "Development of CNN-based visual recognition air conditioner for smart buildings" 25 (25): 361-373, 2020
25 V.J. Mawson, "Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector" 217 : 109966-, 2020
26 J. Bedi, "Deep learning framework to forecast electricity demand" 238 : 1312-1326, 2019
27 A. Javed, "Comparison of the robustness of RNN, MPC and ANN controller for residential heating system" 604-611, 2014
28 International Energy Agency, "CO₂ emissions from fuel combustion"
29 Z. Deng, "Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort" 174 : 587-602, 2018
30 K. O’Shea, "An introduction to convolutional neural networks" 2015
31 Z. Li, "An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage" 12 (12): 665-681, 2019
32 M. Abdel-Nasser, "Accurate photovoltaic power forecasting models using deep LSTM-RNN" 31 (31): 2727-2740, 2019
33 B. Seo, "ANN-based thermal load prediction approach for advanced controls in building energy systems" 2019
34 J. Reynolds, "A zone-level, building energy optimization combining an artificial neural network, a genetic algorithm, and model predictive control" 151 : 729-739, 2018
35 L.J. Lee, "A study on fundamental and application of CNN and RNN" 22 (22): 87-95, 2017
36 H. Luo, "A short-term energy prediction system based on edge computing for smart city" 101 : 444-457, 2019
37 N. Somu, "A Hybrid Model for Building Energy Consumption Forecasting Using Long Short Term Memory Networks" 261 : 114131-, 2020
38 정보통신기술진흥센터, "4차 산업혁명을 선도하는 주요 기술대상 - 기술수준평가 및 기술수준 향상방안-" 2018
Development of a Risk Assessment Model of Rainfall for Small Area in Declining Urban Areas
Global Climate Change and Heat Wave Research from 2010 to 2019 - An Analytical Research Review -
Performance Optimization of a Simplified Passive Design Space in Diversified Climatic Contexts
학술지 이력
연월일 | 이력구분 | 이력상세 | 등재구분 |
---|---|---|---|
2028 | 평가예정 | 재인증평가 신청대상 (재인증) | |
2022-01-01 | 평가 | 등재학술지 유지 (재인증) | |
2019-01-01 | 평가 | 등재학술지 유지 (계속평가) | |
2016-01-01 | 평가 | 등재학술지 유지 (계속평가) | |
2014-02-17 | 학술지명변경 | 한글명 : 한국생태환경건축학회 논문집 -> KIEAE Journal외국어명 : JOURNAL OF THE KOREA INSTITUTE OF ECOLOGICAL ARCHITECTURE AND ENVIRONMENT -> KIEAE Journal | |
2012-01-01 | 평가 | 등재 1차 FAIL (등재유지) | |
2009-01-01 | 평가 | 등재학술지 선정 (등재후보2차) | |
2008-01-01 | 평가 | 등재후보 1차 PASS (등재후보1차) | |
2006-01-01 | 평가 | 등재후보학술지 선정 (신규평가) |
학술지 인용정보
기준연도 | WOS-KCI 통합IF(2년) | KCIF(2년) | KCIF(3년) |
---|---|---|---|
2016 | 0.22 | 0.22 | 0.29 |
KCIF(4년) | KCIF(5년) | 중심성지수(3년) | 즉시성지수 |
0.28 | 0.27 | 0.511 | 0.06 |