User satisfaction-induced demand side load management in residential buildings with user budget constraint

Applied Energy

Volumn 187, Issue , 2017, Pages 352-366

  Ogunjuyigbe, A S O ^a   Ayodele, T R ^a   Akinola, O A ^a  

^a UNIVERSITY OF IBADAN   (Nigeria)

Author keywords

Budget constraint; Demand side load management; Electrical energy; Genetic algorithm; Residential buildings; User satisfaction

Indexed keywords

COSTS; ELECTRIC LOAD MANAGEMENT; ELECTRIC POWER PLANT LOADS; ENERGY UTILIZATION; GENETIC ALGORITHMS; HOUSING;

BUDGET CONSTRAINT; DEMAND-SIDE; ELECTRICAL ENERGY; RESIDENTIAL BUILDING; USER SATISFACTION;

BUDGET CONTROL;

BUILDING; DEMAND-SIDE MANAGEMENT; ELECTRICAL POWER; ELECTRICITY SUPPLY; ENERGY USE; EXPENDITURE; GENETIC ALGORITHM; RESIDENTIAL ENERGY; RESIDENTIAL SATISFACTION;

EID: 85006969594     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.11.071     Document Type: Article

References (31)

1. [1] (UNIDO) UNIDO. Supply-side management. In: Africa Serapmf, editor. UNIDO; 2015.
2. [2] Ogunjuyigbe, A.S.O., Ayodele, T.R., Monyei, C.G., An intelligent load manager for PV powered off-grid residential houses. Energy Sustain Dev 26 (2015), 34–42.
3. [3] Wu, Zhou, Tazvinga, Henerica, Xia, Xiaohua, Demand side management of photovoltaic-battery hybrid system. Appl Energy 148 (2015), 294–304.
4. [4] Campillo, Javier, Dahlquist, Erik, Wallin, Fredrik, Vassileva, Iana, Is real-time electricity pricing suitable for residential users without demand-side management?. Energy 109 (2016), 310–325.
5. [5] El-Baz, Wessam, Tzscheutschler, Peter, Short-term smart learning electrical load prediction algorithm for home energy management systems. Appl Energy 147 (2015), 10–19.
6. [6] Ogunjuyigbe, A.S.O., Ayodele, T.R., Oladimeji, O.E., Management of loads in residential buildings installed with PV system under intermittent solar irradiation using mixed integer linear programming. Energy Build, 2016, 10.1016/j.enbuild.2016.08.042 0378-7788/© 2016 Published by Elsevier Ltd., In Press 1-21.
7. [7] Ravibabu P, Praveen A, Chandra Ch Vikas, Reddy P Rashmi, Teja M Karthik Ravi. An approach of DSM techniques for domestic load management using fuzzy logic. FUZZ-IEEE; 2009.
8. [8] Brooks, A., Lu, E., Reicher, D., Spirakis, C., Weihl, B., Demand dispatch. IEEE Power Energ Mag 8 (2010), 20–29.
9. [9] Ogunjuyigbe, A.S.O., Monyei, C.G., Ayodele, T.R., Price based demand side management: a persuasive smart energy management system for low/medium income earners. Sustain Cities Soc 17 (2015), 80–94.
10. [10] Missaoui, R., Joumaa, H., Ploixa, S., Bacha, S., Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build 71 (2014), 155–167.
11. [11] Alagoz, B.B., Kaygusuz, A., Akcin, M., Alagoz, S., A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market. Energy 59 (2013), 95–104.
12. [12] Murphy, M.D., O'Mahony, M.J., Upton, J., Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment. Appl Energy 149 (2015), 392–403.
13. [13] Di-Giorgio, A., Pimpinella, L., An event driven smart home controller enabling consumer economic saving and automated demand side management. Appl Energy 96 (2012), 92–103.
14. [14] He, Yongxiu, Wang, Bing, Wang, Jianhui, Xiong, Wei, Xiaa, Tian, Residential demand response behavior analysis based on Monte Carlo simulation: the case of Yinchuan in China. Energy 47 (2012), 230–236.
15. [15] Gruber, J.K., Jahromizadeh, S., Prodanović, M., Rakočević, V., Application-oriented modelling of domestic energy demand. Electr Power Energy Syst 61 (2014), 656–664.
16. [16] Soares, Ana, Antunes, Carlos Henggeler, Oliveira, Carlos, Gomes, Álvaro, A multi-objective genetic approach to domestic load scheduling in an energy management system. Energy 77 (2014), 144–152.
17. [17] Shaikh, P.H., Mohd-Nor, N.B., Nallagownden, P., Elamvazuthi, I.E., Ibrahim, T., A review on optimized control systems for building energy and comfort management of smart sustainable building. Renew Sustain Energy 34 (2014), 409–429.
18. [18] Kim, J., Hong, T., Jeong, J., Koo, C., Jeong, K., An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption. Appl Energy 169 (2016), 682–695.
19. [19] Dounis, A.I., Caraiscos, C., Advanced control systems engineering for energy and comfort management in a building environment—a review. Renew Sustain Energy 13 (2009), 1246–1261.
20. [20] Yun, G.Y., Kong, H.J., Kim, H., Kim, J.T., A field survey of visual comfort and lighting energy consumption in open plan offices. Energy Build 46 (2012), 146–151.
21. [21] Pereira, L.D., Raimondo, D., Corgnati, S.P., Gameiro-da-Silva, M., Assessment of indoor air quality and thermal comfort in Portuguese secondary classrooms: methodology and results. Build Environ 81 (2014), 69–80.
22. [22] Balvís, E., Sampedro, O., Zaragoza, S., Paredes, A., Michinel, H., A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings. Appl Energy 177 (2016), 60–70.
23. [23] Korkas, C.D., Baldi, S., Michailidis, I., Kosmatopoulos, E.B., Intelligent energy and thermal comfort management in grid-connected microgrid with heterogeneous occupancy schedule. Appl Energy 149 (2015), 194–203.
24. [24] Korkas, C.D., Baldi, S., Michailidis, I., Kosmatopoulos, E.B., Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage. Appl Energy 163 (2016), 93–104.
25. [25] Yang, R., Wang, L., Development of multi-agent system for building energy and comfort management based on occupant behaviours. Energy Build 56 (2013), 1–7.
26. [26] Mathews, E.H., Arndt, D.C., Piani, C.B., van-Heerden, E., Developing cost effective control strategies to ensure optimal energy use and sufficient indoor comfort. Appl Energy 66 (2000), 135–159.
27. [27] Lawrence, R., Keime, C., Bridging the gap between energy and comfort: post-occupancy evaluation of two higher-education in Sheffield. Energy Build 130 (2016), 651–666.
28. [28] Acosta, I., Campano, M.A., Molina, J.F., Window design in architecture: analysis of energy savings for lighting and visual comfort in residential spaces. Appl Energy 168 (2016), 493–506.
29. [29] Shen, E., Hu, J., Patel, M., Energy and visual comfort analysis of lighting and daylight control strategies. Build Environ 78 (2014), 155–170.
30. [30] Kumar, P., Martani, C., Morawska, L., Norford, L., Choudhary, R., Bell, M., et al. Indoor air quality and energy management through real-time sensing in commercial buildings. Energy Build 111 (2016), 145–153.
31. [31] Shahzad, Sally S., Brennan, John, Theodossopoulos, Dimitris, Hughes, Ben, Calautit, J.K., Energy and comfort in contemporary open plan and traditional personal offices. Energy Procedia 75 (2015), 807–812.