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Erschienen in:
Application of ICT in Parking System Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Enabling Technologies for Smart Energy Management in a Residential Sector: A Review

verfasst von : Mohini Yadav, Majid Jamil, M. Rizwan

Erschienen in: Smart Cities—Opportunities and Challenges

Verlag: Springer Singapore

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Abstract

Continuous rise in energy demand with exposure in the field of smart grid creates new opportunities for energy management in both residential and commercial sector to reduce energy demand. Smart energy management system incorporates the demand response tool to shift and reduce the energy requirement. Further, this system also schedules the energy usage effectively depending on environmental parameters, load consumption profile, user priority index and energy price. Deployment of smart meters creates several opportunities to control the load profile with demand response enabling appliances. Smart energy management has the potential to reduce the carbon emissions with cost-effective energy usage involving renewable energy sources and consumer perspectives. Due to this rising interest toward smart energy management technologies, a review article based on techniques involved in energy monitoring and controlling based on consumer behavior is presented. Further, the implementation of artificial intelligence techniques and optimization approaches involved in optimal load scheduling in a residential sector are also presented.

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Vorheriges Kapitel Application of ICT in Parking System
Nächstes Kapitel Comparative Study of Treatment and Performance in Membrane Bioreactor and Sequencing Batch Reactor for Hospital Wastewater in Smart Cities
Literatur
1.
Shareef H, Ahmed MS, Mohamed A, Al Hassan E (2018) Review on home energy management system considering demand responses, smart technologies, and intelligent controllers. IEEE Access 6:24498–24509
2.
Aman S, Simmhan Y, Prasanna VK (2013) Energy management systems: state of the art and emerging trends. IEEE Commun Mag 51(1):114–119CrossRef
3.
4.
Beaudin M, Zareipour H, Schellenberg A (2012) Residential energy management using a moving window algorithm. In: 2012 3rd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe). IEEE, pp 1–8
5.
Amer M, El-Zonkoly AM, Aziz N, M’Sirdi NK (2014) Smart home energy management system for peak average ratio reduction. Ann Univ Craiova Electr Eng Ser 180–188
6.
Li B, Hathaipontaluk P, Luo S (2009) Intelligent oven in smart home environment. In: International conference on research challenges in computer science, 2009. ICRCCS’09. IEEE, pp 247–250
7.
Choi J, Shin D, Shin D (2005) Research on design and implementation of the artificial intelligence agent for smart home based on support vector machine. In: International conference on natural computation. Springer, Berlin, Heidelberg, pp 1185–1188
8.
Han J, Choi C-S, Lee I (2011) More efficient home energy management system based on ZigBee communication and infrared remote controls. IEEE Trans Consum Electron 57(1)
9.
Pipattanasomporn M, Kuzlu M, Rahman S (2012) An algorithm for intelligent home energy management and demand response analysis. IEEE Trans Smart Grid 3(4):2166–2173CrossRef
10.
Wu X, Hu X, Yin X, Moura SJ (2018) Stochastic optimal energy management of smart home with PEV energy storage. IEEE Trans Smart Grid 9(3):2065–2075CrossRef
11.
Di Giorgio A, Pimpinella L (2012) An event driven smart home controller enabling consumer economic saving and automated demand side management. Appl Energy 96:92–103
12.
Squartini S, Boaro M, De Angelis F, Fuselli D, Piazza F (2013) Optimization algorithms for home energy resource scheduling in presence of data uncertainty. In: 2013 fourth international conference on intelligent control and information processing (ICICIP). IEEE, pp 323–328
13.
Boynuegri AR, Yagcitekin B, Baysal M, Karakas A, Uzunoglu M (2013) Energy management algorithm for smart home with renewable energy sources. In: 2013 fourth international conference on power engineering, energy and electrical drives (POWERENG). IEEE, pp 1753–1758
14.
Dittawit K, Aagesen FA (2013) On adaptable smart home energy systems. In: Power engineering conference (AUPEC)
15.
Zhou S, Wu Z, Li J, Zhang X-P (2014) Real-time energy control approach for smart home energy management system. Electr Power Compon Syst 42(3–4):315–326CrossRef
16.
Missaoui R, Joumaa H, Ploix S, Bacha S (2014) Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build 71:155–167CrossRef
17.
Shahgoshtasbi D, Jamshidi MM (2014) A new intelligent neuro–fuzzy paradigm for energy-efficient homes. IEEE Syst J 8(2):664–673CrossRef
18.
Arcos-Aviles D, Pascual J, Marroyo L, Sanchis P, Guinjoan F (2018) Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans Smart Grid 9(2):530–543CrossRef
19.
Zhang Y, Zeng P, Zang C (2015) Optimization algorithm for home energy management system based on artificial bee colony in smart grid. In: 2015 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER). IEEE, pp 734–740
20.
Hong Y-Y, Chen C-R, Yang H-W (2015) Implementation of demand response in home energy management system using immune clonal selection algorithm. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 3377–3382
21.
Vardakas JS, Zorba N, Verikoukis CV (2015) A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun Surv Tutor 17(1):152–178CrossRef
22.
John JS (2018) Is Europe ready for automated demand response? Online, greentechgrid, Tech Rep, Jan 2018
23.
Ghazvini MAF, Soares J, Abrishambaf O, Castro R, Vale Z (2017) Demand response implementation in smart households. Energy Build 143:129–148CrossRef
24.
Federal Energy Regulatory Commission (2008) Assessment of demand response and advanced metering
25.
Ghazvini MAF, Faria P, Ramos S, Morais H, Vale Z (2015) Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market. Energy 82:786–799CrossRef
26.
Lang C, Okwelum E (2015) The mitigating effect of strategic behavior on the net benefits of a direct load control program. Energy Econ 49:141–148CrossRef
27.
Asadinejad A, Tomsovic K (2017) Optimal use of incentive and price based demand response to reduce costs and price volatility. Electr Power Syst Res 144:215–223CrossRef
28.
Nanda AK, Panigrahi CK (2016) Review on smart home energy management. Int J Ambient Energy 37(5):541–546
29.
Vivekananthan C, Mishra Y, Li F (2015) Real-time price based home energy management scheduler. IEEE Trans Power Syst 30(4):2149–2159CrossRef
30.
Fletcher J, Malalasekera W (2016) Development of a user-friendly, low-cost home energy monitoring and recording system. Energy 111:32–46CrossRef
31.
Keskin ME (2017) A column generation heuristic for optimal wireless sensor network design with mobile sinks. Eur J Oper Res 260(1):291–304MathSciNetMATHCrossRef
32.
Peng C, Huang J (2016) A home energy monitoring and control system based on ZigBee technology. Int J Green Energy 13(15):1615–1623MathSciNetCrossRef
33.
Collotta M, Pau G (2015) A novel energy management approach for smart homes using bluetooth low energy. IEEE J Sel Areas Commun 33(12):2988–2996CrossRef
34.
Amzucu DM, Li H, Fledderus E (2014) Indoor radio propagation and interference in 2.4 GHz wireless sensor networks: measurements and analysis. Wirel Pers Commun 76(2):245–269
35.
Khan AA, Razzaq S, Khan A, Khursheed F (2015) HEMSs and enabled demand response in electricity market: an overview. Renew Sustain Energy Rev 42:773–785
36.
Wang L, Peng D, Zhang T (2015) Design of smart home system based on WiFi smart plug. Int J Smart Home 9(6):173–182CrossRef
37.
Ojuroye O, Torah R, Beeby S, Wilde A (2017) Smart textiles for smart home control and enriching future wireless sensor network data. In: Sensors for everyday life. Springer, Cham, pp 159–183
38.
Abubakar I, Khalid SN, Mustafa MW, Shareef H, Mustapha M (2017) Application of load monitoring in appliances’ energy management—a review. Renew Sustain Energy Rev 67:235–245
39.
Pipattanasomporn M, Kuzlu M, Khamphanchai W, Saha A, Rathinavel K, Rahman S (2015) BEMOSS™: an agent platform to facilitate grid-interactive building operation with IoT devices. In: 2015 IEEE PES innovative smart grid technologies conference Asia (ISGT-Asia), 4–6 Nov 2015, pp 1–6
40.
Marzband M, Alavi H, Ghazimirsaeid SS, Uppal H, Fernando T (2017) Optimal energy management system based on stochastic approach for a home microgrid with integrated responsive load demand and energy storage. Sustain Cities Soc 28:256–264
41.
Zhou B, Li W, Chan KW, Cao Y, Kuang Y, Liu X, Wang X (2016) Smart home energy management systems: concept, configurations, and scheduling strategies. Renew Sustain Energy Rev 61:30–40
42.
Yuce B, Rezgui Y, Mourshed M (2016) ANN–GA smart appliance scheduling for optimised energy management in the domestic sector. Energy Build 111:311–325CrossRef
43.
Liu Y, Yuen C, Yu R, Zhang Y, Xie S (2016) Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans Smart Grid 7(3):1650–1659CrossRef
44.
Önder Z, Sezer SA, Çanak İ (2015) A Tauberian theorem for the weighted mean method of summability of sequences of fuzzy numbers. J Intell Fuzzy Syst Appl Eng Technol 28(3):1403–1409
45.
Wu Z, Zhou S, Li J, Zhang X-P (2014) Real-time scheduling of residential appliances via conditional risk-at-value. IEEE Trans Smart Grid 5(3):1282–1291CrossRef
46.
Premkumar K, Manikandan BV (2015) Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing 157:76–90CrossRef
47.
Choi IH, Yoo SH, Jung JH, Lim MT, Oh JJ, Song MK, Ahn CK (2015) Design of neuro-fuzzy based intelligent inference algorithm for energy-management system with legacy device. Trans Korean Inst Electr Eng 64(5):779–785
48.
Mohsenian-Rad A-H, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1(2):120–133CrossRef
49.
Guo Y, Pan M, Fang Y (2012) Optimal power management of residential customers in the smart grid. IEEE Trans Parallel Distrib Syst 23(9):1593–1606CrossRef
50.
Wang Z, Yang R, Wang L (2010) Multi-agent control system with intelligent optimization for smart and energy-efficient buildings. In: IECON 2010-36th annual conference on IEEE industrial electronics society. IEEE, pp 1144–1149
51.
Rasheed MB, Javaid N, Ahmad A, Khan ZA, Qasim U, Alrajeh N (2015) An efficient power scheduling scheme for residential load management in smart homes. Appl Sci 5(4):1134–1163
52.
Mahmood D, Javaid N, Alrajeh N, Khan ZA, Qasim U, Ahmed I, Ilahi M (2016) Realistic scheduling mechanism for smart homes. Energies 9(3):202
Metadaten
Titel
Enabling Technologies for Smart Energy Management in a Residential Sector: A Review
verfasst von
Mohini Yadav
Majid Jamil
M. Rizwan
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Smart Cities—Opportunities and Challenges

Print ISBN: 978-981-15-2544-5

Electronic ISBN: 978-981-15-2545-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-2545-2_2

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