پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,012 |
| تعداد دریافت فایل اصل مقاله | 4,882,748 |
Learning-Based Energy Management System for Scheduling of Appliances inside Smart Homes | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 10، دوره 51، شماره 2، اسفند 2019، صفحه 211-218 اصل مقاله (594.9 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2019.16892.5296 | ||
| نویسندگان | ||
| Nastaran Gholizadeh1؛ Mehrdad Abedi2؛ Hamed Nafisi* 3؛ Mousa Marzband4 | ||
| 1Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran | ||
| 2Amirkabir University of Technology | ||
| 3AUT | ||
| 4Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle, United Kingdom | ||
| چکیده | ||
| Improper designs of the demand response programs can lead to numerous problems such as customer dissatisfaction and lower participation in these programs. In this paper, a home energy management system is designed which schedules appliances of smart homes based on the user’s specific behavior to address these issues. Two types of demand response programs are proposed for each house which are shifting-based and learning-based programs for shiftable and heating, ventilation and cooling appliances, respectively. The current structure uses machine learning techniques to design the best demand response programs for heating, ventilation and cooling devices of each user based on his/her behavior and desired comfort level. Doing so, the home energy management system is able to achieve energy cost and consumption reduction without causing dissatisfaction and discomfort to the users. Results demonstrate that by using this structure, energy cost and consumption are reduced by 20.32% and 27%, respectively for a single house located in the Austin, Texas area, in one day. The proposed home energy management structure is tested on three additional houses to show the effectiveness of it. Moreover, comparisons with other methods are performed to clarify the benefits of this structure over other methods. The proposed structure is formulated as a mixed-integer linear model with its optimization performed in the General Algebraic Modeling System environment. CPLEX solver is used to solve the optimization problem. | ||
| کلیدواژه‌ها | ||
| Demand response؛ Home energy management system؛ Machine learning؛ Mixed-integer linear programming؛ User behavior | ||
| مراجع | ||
|
[1] H. Kikusato, K. Mori, S. Yoshizawa, Y. Fujimoto, H. Asano, Y. Hayashi, A. Kawashima, S. Inagaki, T. Suzuki, Electric Vehicle Charge–Discharge Management for Utilization of Photovoltaic by Coordination Between Home and Grid Energy Management Systems, IEEE Transactions on Smart Grid, 10(3) (2019) 3186-3197. [2] H. Shareef, M.S. Ahmed, A. Mohamed, E.A. Hassan, Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers, IEEE Access, 6 (2018) 24498-24509. [3] M. Yousefi, A. Hajizadeh, M.N. Soltani, A Comparison Study on Stochastic Modeling Methods for Home Energy Management Systems, IEEE Transactions on Industrial Informatics, 15(8) (2019) 4799-4808. [4] F. Luo, G. Ranzi, C. Wan, Z. Xu, Z.Y. Dong, A Multistage Home Energy Management System With Residential Photovoltaic Penetration, IEEE Transactions on Industrial Informatics, 15(1) (2019) 116-126. [5] G. Dong, Z. Chen, Data-Driven Energy Management in a Home Microgrid Based on Bayesian Optimal Algorithm, IEEE Transactions on Industrial Informatics, 15(2) (2019) 869-877. [6] B. Celik, R. Roche, D. Bouquain, A. Miraoui, Decentralized Neighborhood Energy Management With Coordinated Smart Home Energy Sharing, IEEE Transactions on Smart Grid, 9(6) (2018) 6387-6397. [7] H. Huang, Y. Cai, H. Xu, H. Yu, A Multiagent Minority-Game-Based Demand-Response Management of Smart Buildings Toward Peak Load Reduction, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 36(4) (2017) 573-585. [8] T. Molla, B. Khan, B. Moges, H.H. Alhelou, R. Zamani, P. Siano, Integrated optimization of smart home appliances with cost-effective energy management system, CSEE Journal of Power and Energy Systems, 5(2) (2019) 249-258. [9] P. Zhao, H. Wu, C. Gu, I. Hernando-Gil, Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach, IET Renewable Power Generation, 13(11) (2019) 1911-1919. [10] K.M. Tsui, S.C. Chan, Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing, IEEE Transactions on Smart Grid, 3(4) (2012) 1812-1821. [11] S. Althaher, P. Mancarella, J. Mutale, Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints, IEEE Transactions on Smart Grid, 6(4) (2015) 1874-1883. [12] S. Lee, B. Kwon, S. Lee, Joint Energy Management System of Electric Supply and Demand in Houses and Buildings, IEEE Transactions on Power Systems, 29(6) (2014) 2804-2812. [13] M. Shafie-Khah, P. Siano, A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue, IEEE Transactions on Industrial Informatics, 14(2) (2018) 629-638. [14] A. Anvari-Moghaddam, H. Monsef, A. Rahimi-Kian, Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle, IEEE Transactions on Smart Grid, 6(1) (2015) 324-332. [15] H.T. Nguyen, D.T. Nguyen, L.B. Le, Energy Management for Households With Solar Assisted Thermal Load Considering Renewable Energy and Price Uncertainty, IEEE Transactions on Smart Grid, 6(1) (2015) 301-314. [16] M. Pipattanasomporn, M. Kuzlu, S. Rahman, An Algorithm for Intelligent Home Energy Management and Demand Response Analysis, IEEE Transactions on Smart Grid, 3(4) (2012) 2166-2173. [17] P. Chavali, P. Yang, A. Nehorai, A Distributed Algorithm of Appliance Scheduling for Home Energy Management System, IEEE Transactions on Smart Grid, 5(1) (2014) 282-290. [18] A. Naeem, A. Shabbir, N.U. Hassan, C. Yuen, A. Ahmad, W. Tushar, Understanding Customer Behavior in Multi-Tier Demand Response Management Program, IEEE Access, 3 (2015) 2613-2625. [19] P. Samadi, H. Mohsenian-Rad, V.W.S. Wong, R. Schober, Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid, IEEE Transactions on Smart Grid, 4(2) (2013) 1007-1016. [20] X. Chen, T. Wei, S. Hu, Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home, IEEE Transactions on Smart Grid, 4(2) (2013) 932-941. [21] Z. Zhou, F. Zhao, J. Wang, Agent-Based Electricity Market Simulation With Demand Response From Commercial Buildings, IEEE Transactions on Smart Grid, 2(4) (2011) 580-588. [22] S. Li, D. Zhang, A.B. Roget, Z.O. Neill, Integrating Home Energy Simulation and Dynamic Electricity Price for Demand Response Study, IEEE Transactions on Smart Grid, 5(2) (2014) 779-788. [23] L. Yu, T. Jiang, Y. Zou, Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy, IEEE Transactions on Smart Grid, 10(2) (2019) 1646-1659. [24] D. Zhang, S. Li, M. Sun, Z. O’Neill, An Optimal and Learning-Based Demand Response and Home Energy Management System, IEEE Transactions on Smart Grid, 7(4) (2016) 1790-1801. [25] O. Longe, K. Ouahada, S. Rimer, H. Ferreira, A. J. Han Vinck, Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid, Sustainability, 9 (2017) 1088. [26] M. Carrion, J.M. Arroyo, A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem, IEEE Transactions on Power Systems, 21(3) (2006) 1371-1378. [27] Pecan Street Inc., http://dataport.cloud/.com, October 2015 - April 2018. | ||
|
آمار تعداد مشاهده مقاله: 1,348 تعداد دریافت فایل اصل مقاله: 243 |
||