최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기정보처리학회논문지. KIPS transactions on software and data engineering. 소프트웨어 및 데이터 공학, v.9 no.5, 2020년, pp.153 - 160
(순천대학교 정보통신멀티미디어공학부) , 이명배 (순천대학교 정보통신멀티미디어공학부) , 임종현 (순천대학교 정보통신멀티미디어공학부) , 김유빈 ((주)엘시스) , 신창선 (순천대학교 정보통신멀티미디어공학부) , 박장우 (순천대학교 정보통신멀티미디어공학부) , 조용윤 (순천대학교 정보통신멀티미디어공학부)
Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data ...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
C. Oluklulu, "A Research on the Photovoltaic Modules That Are Being Used Actively in Utilizing Solar Energy, Sizing of the Modules and Architectural Using Means of the Modules," Master's Thesis, Gazi University, Ankara, Turkey, 2001.
V. I. Ugursal, "Energy Consumption, Associated Questions and some Answers," Appl. Energy, Vol. 130, pp. 783-792, 2014.
Rinkesh, What is the Energy Crisis. Available [Internet], https://www.conserve-energy-future.com/causesand-solutions-to-the-global-energy-crisis.php#abh_posts (accessed on 25 Jan. 2019).
D. Streimikiene, "Residential Energy Consumption Trends, Main Drivers and Policies in Lithuania," Renew. Sustain. Energy Rev, Vol. 35, pp. 285-293, 2014.
J. Zuo and Z. Y. Zhao, "Green Building Research-Current Status and Future Agenda: A review. Renew. Sustain," Energy Rev., Vol. 30, pp. 271-281, 2014.
Seung-Moon, Lee, "Mid-term Korea Energy Demand Outlook," Korea Energy Economics Institute, May 2014.
L. Ekonomou, “Greek long-term energy consumption prediction using artificial neural networks,” Energy, Vol. 35, No. 2, pp. 512-517, 2010.
G. Munz, S. Li, and G. Carle, "Traffic Anomaly Detection Using k-means Clustering," In Proceedings of the GI/ITG Workshop MMBnet, Hamburg, Germany, pp. 13-14, Sep. 2007.
K. Kandananond, “Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach,” Energies, Vol. 4, No. 12, pp. 1246-1257, 2011.
C. De Cauwer, J. Van Mierlo, and T. Coosemans, “Energy Consumption Prediction for Electric Vehicles based on Real-world Data,” Energies, Vol. 8, No. 8, pp. 8573-8593, 2015.
B. Dong, C. Cao, and S. E. Lee, “Applying Support Vector Machines to Predict Building Energy Consumption in Tropical Region,” Energy Build., Vol. 37, No. 5, pp. 545-553, 2005.
P. A. Gonzalez, and J. M. Zamarreno, “Prediction of Hourly Energy Consumption in Buildings Based on a Feedback Artificial Neural Network,” Energy Build., Vol. 37, No. 6, pp. 595-601, 2005.
B. B. Ekici and U. T. Aksoy, “Prediction of Building Energy Consumption by Using Artificial Neural Networks,” Adv. Eng. Softw., Vol. 40, No. 5, pp. 356-362, 2009.
Q. Li, P. Ren, and Q. Meng, "Prediction Model of Annual Energy Consumption of Residential Buildings," In Proceedings of the 2010 International Conference on Advances in Energy Engineering, Beijing, China, pp. 223-226, 2010.
L. Xuemei, D. Yuyan, D. Lixing, and J. Liangzhong, "Building Cooling Load Forecasting Using Fuzzy Support Vector Machine and Fuzzy C-mean Clustering," In Proceedings of the 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, Chengdu, pp. 438-441, 2010.
Y. Ma, J. Q. Yu, C. Y. Yang, and L. Wang, "Study on Power Energy Consumption Model for Large-scale Public Building," In Proceedings of the 2010 2nd International Workshop on. IEEE Intelligent Systems and Applications, Wuhan, pp. 1-4, 2010.
J. Zhao, Z. Han, W. Pedrycz, and W. Wang, “Granular model of long-term prediction for energy system in steel industry,” IEEE Transactions on Cybernetics, Vol. 46, No. 2, pp. 388-400, 2015.
Y. Zhang, X. Zhang, and L. Tang, "Energy consumption prediction in ironmaking process using hybrid algorithm of SVM and PSO," In International Symposium on Neural Networks, pp. 594-600, 2012.
B. Zeng, M. Zhou, and J. Zhang, “Forecasting the energy consumption of China’s manufacturing using a homologous grey prediction model,” Sustainability, Vol. 9, No. 11, pp. 1-16, 2017.
P. Sen, M. Roy, and P. Pal., “Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization,” Energy, Vol. 116, No. 1, pp. 1031-1038, 2016.
J. Reimann, "Methodology and model for predicting energy consumption in manufacturing at multiple scales," Procedia Manufacturing, Vol. 21, pp. 694-701, 2018.
J. Zhou, E. Li, H. Wei, C. Li, Q. Qiao, and D. J. Armaghani, “Random forests and cubist algorithms for predicting shear strengths of rockfill materials,” Applied Sciences, Vol. 9, No. 8, pp. 1-16, 2019.
J. D. Rodriguez, A. Perez, and J. A. Lozano, “Sensitivity analysis of k-fold cross validation in prediction error estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 3, pp. 569-575, 2009.
R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” In Ijcai, Vol. 14, No. 2, pp. 1137-1145, 1995.
T. T. Wong, “Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation,” Pattern Recognition, Vol. 48, No. 9, pp. 2839-2846, 2015.
R. C. Team, "R: A language and environment for statistical computing," 201, 2013.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.