A Novel Generalised Model for Residential Energy Management System
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, 31 Symonds Street, Auckland CBD, Auckland 1010, New Zealand
Academic Editor: Hegazy Rezk
Highlights Sustain. 2022, 1(3), 134–158.
Received: 25 April 2022 Accepted: 5 July 2022 Published: 11 July 2022
This article is part of the Special Issue Energy Efficiency and Renewable Energy.
Disaggregated data is often used to model the cost-benefit of residential energy management systems. However, obtaining such data is time-intensive and monetarily expensive. This hinders the depth of analysis that can be done on these systems and negatively influences their large-scale uptake. This study proposes a novel generalised model of these systems that uses smart meter load profile data to model their cost-benefit. Using two years of half-hourly electricity consumption data from 5379 households in London, the model was used to examine how sociodemographic, tariff structures, and the choice of operational objectives of these systems, interact to influence their cost-benefit. The results showed that the proposed model produced reliable cost-benefit results within what is normally obtained in literature. The model demonstrated that applying one set of objectives to different customers leads to an inequitable distribution in benefits; rather, an optimal set of objectives for a given customer under a specific tariff structure can be found to produce a more equitable distribution in benefits across all customers. The proposed model is replicable and uses data that can be obtained easily and cheaply from smart meters, making it versatile for large-scale cost-benefit analysis by any electricity retailer.
Figures in this Article
Copyright © 2022 Jean-Paul et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use and distribution provided that the original work is properly cited.
Cite this Article
Jean-Paul, P.; Lie, T.T.; Anderson, T.N.; Vallès, B. A Novel Generalised Model for Residential Energy Management System. Highlights Sustain. 2022, 1, 134–158. https://doi.org/10.54175/hsustain1030011
Jean-Paul, P., Lie, T. T., Anderson, T. N., & Vallès, B. (2022). A Novel Generalised Model for Residential Energy Management System. Highlights of Sustainability, 1(3), 134–158. https://doi.org/10.54175/hsustain1030011
Chen, Y.-T. The factors affecting electricity consumption and the consumption characteristics in the residential sector—a case example of Taiwan. Sustainability 2017, 9, 1484. https://doi.org/10.3390/su9081484
Beaudin, M.; Zareipour, H. Home energy management systems: A review of modelling and complexity. Renew. Sust. Energ. Rev. 2015, 45, 318–335. https://doi.org/10.1016/j.rser.2015.01.046
Anjana, K.R.; Shaji, R.S. A review on the features and technologies for energy efficiency of smart grid. Int. J. Energy Res. 2018, 42, 936–952. https://doi.org/10.1002/er.3852
Habib, A. Overview of Leading Methodologies for Demand Response in Smart Grid and Demand Response case overview in China and abroad. Int. J. Novel Res. Comput. Sci. Softw. Eng. 2017, 4, 19–34.
Gutierrez-Martinez, V.J.; Moreno-Bautista, C.A.; Lozano-Garcia, J.M.; Pizano-Martinez, A.; Zamora-Cardenas, E.A.; Gomez-Martinez, M.A. A heuristic home electric energy management system considering renewable energy availability. Energies 2019, 12, 671. https://doi.org/10.3390/en12040671
İzmitligil, H.; Apaydn Özkan, H. A home energy management system. Trans. Inst. Meas. Control 2018, 40, 2498–2508. https://doi.org/10.1177/0142331217741537
Hartono, B.; Mursid, S.P.; Prajogo, S. Home energy management system in a Smart Grid scheme to improve reliability of power systems. IOP Conf. Ser.: Earth Environ. Sci. 2018, 105. 012081. https://doi.org/10.1088/1755-1315/105/1/012081
Zunnurain, I.; Maruf, M.; Islam, N.; Rahman, M.; Shafiullah, G. Implementation of advanced demand side management for microgrid incorporating demand response and home energy management system. Infrastructures 2018, 3, 50. https://doi.org/10.3390/infrastructures3040050
Lazowski, B.; Parker, P.; Rowlands, I.H. Towards a smart and sustainable residential energy culture: assessing participant feedback from a long-term smart grid pilot project. Energy Sustain. Soc. 2018, 8, 27. https://doi.org/10.1186/s13705-018-0169-9
Loganathan, N.; Mayurappriyan, P.; Lakshmi, K. Smart energy management systems: a literature review. MATEC Web Conf. 2018, 225, 01016. https://doi.org/10.1051/matecconf/201822501016
Latif, U.; Javaid, N.; Zarin, S.S.; Naz, M.; Jamal, A.; Mateen, A. Cost Optimization in Home Energy Management System using Genetic Algorithm, Bat Algorithm and Hybrid Bat Genetic Algorithm. IEEE AINA 2018 2018, 667–677. https://doi.org/10.1109/AINA.2018.00102
Wang, J.; Li, P.; Fang, K.; Zhou, Y. Robust Optimization for Household Load Scheduling with Uncertain Parameters. Appl. Sci. 2018, 8, 575. https://doi.org/10.3390/app8040575
Najafi, F.; Fripp, M. Stochastic optimization of comfort-centered model of electrical water heater using mixed integer linear programming. Sustain. Energy Technol. Assess. 2020, 42, 100834. https://doi.org/10.1016/j.seta.2020.100834
Alam, M.S.; Arefifar, S.A. Energy Management in Power Distribution Systems: Review, Classification, Limitations and Challenges. IEEE Access 2019, 7, 92979–93001. https://doi.org/10.1109/ACCESS.2019.2927303
Gonçalves, I.; Gomes, Á.; Antunes, C.H. Optimizing the management of smart home energy resources under different power cost scenarios. Appl. Energy 2019, 242, 351–363. https://doi.org/10.1016/j.apenergy.2019.03.108
Jamil, A.; Alghamdi, T.A.; Khan, Z.A.; Javaid, S.; Haseeb, A.; Wadud, Z.; Javaid, N. An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory. Sustainability 2019, 11, 6287. https://doi.org/10.3390/su11226287
Barker, S.; Morrison, K.; Williams, T. Exploiting Breadth in Energy Datasets for Automated Device Identification. IEEE SmartGridComm 2019 2019, 1–6. https://doi.org/10.1109/SmartGridComm.2019.8909725
Lu, Q.; Lü, S.; Leng, Y.; Zhang, Z. Optimal household energy management based on smart residential energy hub considering uncertain behaviors. Energy 2020, 195, 117052. https://doi.org/10.1016/j.energy.2020.117052
Shirazi, E.; Zakariazadeh, A.; Jadid, S. Optimal joint scheduling of electrical and thermal appliances in a smart home environment. Energy Convers. Manag. 2015, 106, 181–193. https://doi.org/10.1016/j.enconman.2015.09.017
Killian, M.; Zauner, M.; Kozek, M. Comprehensive smart home energy management system using mixed-integer quadratic-programming. Appl. Energy 2018, 222, 662–672. https://doi.org/10.1016/j.apenergy.2018.03.179
Najafi, B.; Moaveninejad, S.; Rinaldi, F. Data analytics for energy disaggregation: methods and applications. In Big Data Application in Power Systems; Arghandeh, R., Zhou, Y., Eds.; Elsevier: Amsterdam, The Netherlands, 2018. pp. 377–408. https://doi.org/10.1016/B978-0-12-811968-6.00017-6
Yao, L.; Damiran, Z.; Lim, W.H. Energy management optimization scheme for smart home considering different types of appliances. IEEE EEEIC/I&CPS Europe 2017 2017, 1–6. https://doi.org/10.1109/EEEIC.2017.7977565
Wu, X.; Hu, X.; Yin, X.; Zhang, C.; Qian, S. Optimal battery sizing of smart home via convex programming. Energy 2017, 140, 444–453. https://doi.org/10.1016/j.energy.2017.08.097
Shafie-Khah, M.; Siano, P. A stochastic home energy management system considering satisfaction cost and response fatigue. IEEE Trans. Industr. Inform. 2018, 14, 629–638. https://doi.org/10.1109/TII.2017.2728803
Veras, J.M.; Silva, I.R.S.; Pinheiro, P.R.; Rabêlo, R.A.L.; Veloso, A.F.S.; Borges, F.A.S.; Rodrigues, J.J.P.C. A Multi-Objective Demand Response Optimization Model for Scheduling Loads in a Home Energy Management System. Sensors 2018, 18, 3207. https://doi.org/10.3390/s18103207
Hoyo-Montaño, J.A.; León-Ortega, N.; Valencia-Palomo, G.; Galaz-Bustamante, R.A.; Espejel-Blanco, D.F.; Vázquez-Palma, M.G. Non-intrusive electric load identification using wavelet transform. Ing. Investig. 2018, 38, 42–51. https://doi.org/10.15446/ing.investig.v38n2.70550
Eskander, M.M.; Silva, C.A. A complementary unsupervised load disaggregation method for residential loads at very low sampling rate data. Sustain. Energy Technol. Assess. 2021, 43, 100921. https://doi.org/10.1016/j.seta.2020.100921
Zhao, C.; Dong, S.; Li, F.; Song, Y. Optimal home energy management system with mixed types of loads. CSEE J. Power Energy Syst. 2015, 1, 29–37. https://doi.org/10.17775/CSEEJPES.2015.00045
Issi, F.; Kaplan, O. The Determination of Load Profiles and Power Consumptions of Home Appliances. Energies 2018, 11, 607. https://doi.org/10.3390/en11030607
Mehdipour Pirbazari, A.; Farmanbar, M.; Chakravorty, A.; Rong, C. Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis. Processes 2020, 8, 484. https://doi.org/10.3390/pr8040484
Panda, M. Intelligent data analysis for sustainable smart grids using hybrid classification by genetic algorithm based discretization. Intell. Decis. Technol. 2017, 11, 137–151. https://doi.org/10.3233/IDT-170283
Pflugradt, N. Load Profile Generator. Available online: https://www.loadprofilegenerator.de (accessed 7 July 2022).
Gajowniczek, K.; Ząbkowski, T. Electricity forecasting on the individual household level enhanced based on activity patterns. PLoS One 2017, 12, e0174098. https://doi.org/10.1371/journal.pone.0174098
Beaudin, M.; Zareipour, H. Home energy management systems: A review of modelling and complexity. In Energy Solutions to Combat Global Warming; Zhang, X., Dincer, I., Eds.; Lecture Notes in Energy, vol 33; Springer, Cham, Switzerland, 2017; pp. 753–793. https://doi.org/10.1007/978-3-319-26950-4_35
Washizu, A.; Nakano, S.; Ishii, H.; Hayashi, Y. Willingness to Pay for Home Energy Management Systems: A Survey in New York and Tokyo. Sustainability 2019, 11, 4790. https://doi.org/10.3390/su11174790
Westin, K.; Jansson, J.; Nordlund, A. The importance of socio-demographic characteristics, geographic setting, and attitudes for adoption of electric vehicles in Sweden. Travel Behav. Soc. 2018, 13, 118–127. https://doi.org/10.1016/j.tbs.2018.07.004
Khalid, A.; Javaid, N.; Mateen, A.; Ilahi, M.; Saba, T.; Rehman, A. Enhanced Time-of-Use Electricity Price Rate Using Game Theory. Electronics 2019, 8, 48. https://doi.org/10.3390/electronics8010048
Koolen, D.; Sadat-Razavi, N.; Ketter, W. Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing. Appl. Sci. 2017, 7, 1160. https://doi.org/10.3390/app7111160
Ji, W.; Chan, E.H.W. Critical Factors Influencing the Adoption of Smart Home Energy Technology in China: A Guangdong Province Case Study. Energies 2019, 12, 4180. https://doi.org/10.3390/en12214180
Voulis, N.; van Etten, M.J.; Chappin, É.J.; Warnier, M.; Brazier, F.M. Rethinking European energy taxation to incentivise consumer demand response participation. Energy Policy 2019, 124, 156–168. https://doi.org/10.1016/j.enpol.2018.09.018
Dehdarian, A. Scenario-based system dynamics modeling for the cost recovery of new energy technology deployment: The case of smart metering roll-out. J. Clean. Prod. 2018, 178, 791–803. https://doi.org/10.1016/j.jclepro.2017.12.253
Balta-Ozkan, N.; Davidson, R.; Bicket, M.; Whitmarsh, L.J.E. The development of smart homes market in the UK. Energy 2013, 60, 361–372. https://doi.org/10.1016/j.energy.2013.08.004
Asare-Bediako, B.; Kling, W.; Ribeiro, P. Home energy management systems: Evolution, trends and frameworks. IEEE UPEC 2012 2012, 1–5. https://doi.org/10.1109/UPEC.2012.6398441
Erickson, L.E. Reducing greenhouse gas emissions and improving air quality: Two global challenges. Environ. Prog. Sustain. Energy 2017, 36, 982–988. https://doi.org/10.1002/ep.12665
Ozaki, R. Follow the price signal: people’s willingness to shift household practices in a dynamic time-of-use tariff trial in the United Kingdom. Energy Res. Soc. Sci. 2018, 46, 10–18. https://doi.org/10.1016/j.erss.2018.06.008
Bilton, M.; Carmichael, R.; Whitney, A.; Dragovic, J.; Schofield, J.; Woolf, M. Accessiblity and validity of smart meter data. Report C5 for the “Low Carbon London” LCNF project: Imperial College London, 2014. Available online: https://innovation.ukpowernetworks.co.uk/wp-content/uploads/2019/05/C5-Accessibility-and-Validity-of-Smart-Meter-Data.pdf (accessed 16 September 2019).
Bilton, M.; Woolf, M.; Djapic, P.; Aunedi, M.; Carmichael, R.; Strbac, G. Impact of energy efficient appliances on network utilisation. Report C2 for the “Low Carbon London” LCNF project: Imperial College London, 2014. Available online: https://innovation.ukpowernetworks.co.uk/wp-content/uploads/2019/05/LCL-Learning-Report-C2-Impact-of-energy-efficient-appliances-on-network-utilisation.pdf (accessed 7 July 2022).
Sun, M.; Konstantelos, I.; Strbac, G. C-vine copula mixture model for clustering of residential electrical load pattern data. IEEE Trans. Power Syst. 2016, 32, 2382–2393. https://doi.org/10.1109/TPWRS.2016.2614366
Kim, Y.; Son, H.-g.; Kim, S. Short term electricity load forecasting for institutional buildings. Energy Rep. 2019, 5, 1270–1280. https://doi.org/10.1016/j.egyr.2019.08.086
Cheng, C.-C.; Lee, D. Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis. Sensors 2019, 19, 1131. https://doi.org/10.3390/s19051131
Bayram, I.S.; Ustun, T.S. A survey on behind the meter energy management systems in smart grid. Renew. Sust. Energ. Rev. 2017, 72, 1208–1232. https://doi.org/10.1016/j.rser.2016.10.034
Hajibandeh, N.; Shafie-Khah, M.; Osório, G.J.; Aghaei, J.; Catalão, J.P. A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators. Appl. Energy 2018, 212, 721–732. https://doi.org/10.1016/j.apenergy.2017.12.076
Gazafroudi, A.S.; Soares, J.; Ghazvini, M.A.F.; Pinto, T.; Vale, Z.; Corchado, J.M. Stochastic interval-based optimal offering model for residential energy management systems by household owners. Int. J. Electr. Power Energy Syst. 2019, 105, 201–219. https://doi.org/10.1016/j.ijepes.2018.08.019
Heidarinejad, G.; Shokrollahi, S.; Pasdarshahri, H. An investigation of thermal comfort, IAQ, and energy saving in UFAD systems using a combination of Taguchi optimization algorithm and CFD. Adv. Build. Energy Res. 2020, 1–19. https://doi.org/10.1080/17512549.2020.1784276
Lin, G.; Yang, Y.; Pan, F.; Zhang, S.; Wang, F.; Fan, S. An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality. Future Internet 2019, 11, 88. https://doi.org/10.3390/fi11040088
Matteini, M.; Pasqualetto, G.; Petrovska, A. Cost-benefit analysis of energy management systems implementation at enterprise and programme level. In Industrial Efficiency 2018, eceee Industrial Summer Study Proceedings, Berlin, Germany, 11–13 June 2018; ECEEE: Stockholm, Sweden, 2018.
Terlouw, T. AlSkaif, T.; Bauer, C.; van Sark, W. Optimal energy management in all-electric residential energy systems with heat and electricity storage. Appl. Energy 2019, 254, 113580. https://doi.org/10.1016/j.apenergy.2019.113580
Ahmad, A.; Khan, A.; Javaid, N.; Hussain, H.M.; Abdul, W.; Almogren, A.; Alamri, A.; Azim Niaz, I. An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources. Energies 2017, 10, 549. https://doi.org/10.3390/en10040549
Ahmed, M.S.; Mohamed, A.; Shareef, H.; Homod, R.Z.; Abd Ali, J. Artificial neural network based controller for home energy management considering demand response events. IEEE ICAEES 2016 2016, 506–509. https://doi.org/10.1109/ICAEES.2016.7888097
Chojecki, A.; Rodak, M.; Ambroziak, A.; Borkowski, P. Energy management system for residential buildings based on fuzzy logic: design and implementation in smart-meter. IET Smart Grid 2020, 3, 254–266. https://doi.org/10.1049/iet-stg.2019.0005
Jalili, H.; Sheikh-El-Eslami, M.K.; Moghaddam, M.P.; Siano, P. Modeling of demand response programs based on market elasticity concept. J. Ambient. Intell. Humaniz. Comput. 2019, 10, 2265–2276. https://doi.org/10.1007/s12652-018-0821-4
C.A.C.I. Understanding Consumers and Communities. Available online: https://acorn.caci.co.uk/what-is-acorn (accessed 22 April 2018).