FLEXIBLE LOAD SCHEDULING IN A HOUSEHOLD CYBER-PHYSICAL SYSTEM WITH SOLAR GENERATION: DEPENDABILITY OF DECISIONS UNDER INPUT DATA UNCERTAINTY
DOI:
https://doi.org/10.28925/2663-4023.2026.35.1448Keywords:
flexible load scheduling, cyber-physical system, home energy management system, dependability of decisions, forecast uncertainty, genetic algorithm, solar generation, demand responseAbstract
Household energy management systems decide when appliances operate on the basis of data from external and not always reliable sources: weather forecasts, tariffs and appliance descriptions entered by the user. An error in, or the unavailability of, any of these sources can make a schedule that is advantageous on paper unprofitable or even more expensive than the habitual consumption pattern. The paper proposes a day-ahead flexible load scheduling method in which a genetic algorithm searches for the start times of non-interruptible appliance runs, while interruptible loads are allocated by a deterministic greedy procedure inside the evaluation of the objective function. The method is supplemented with five mechanisms that improve the dependability of decisions: stepwise degradation of the generation forecast source, exclusion of appliances with incomplete descriptions from scheduling, a guarantee that the plan is not more expensive than the baseline schedule, returning a list of violated constraints instead of failing, and storing a snapshot of the input data so that each decision can be reproduced. The method was studied on a model of a real household with a 4 kW photovoltaic plant under single-rate, two-zone and three-zone tariffs. With accurate data, the plan reduces electricity cost by 18.5–26.9 % under zone tariffs without the plant and by about half on a clear day. Under the real error of an archived generation forecast over seven days, the plan retained about 75 % of the savings achievable with a perfect forecast, and about 71 % when falling back to a clear-sky model. The exact optimum obtained by mixed-integer linear programming outperforms the method by only 0.5–2.0 percentage points on average under the real forecast and yields lower savings on the worst day, whereas simple scheduling rules produce negative savings on the same days. Relative to actual consumption, when the residents already shifted loads manually, the savings were 14.4–22.1 %, so a mismatch between appliance descriptions and reality noticeably changes the expected benefit. The guarantee of not exceeding the baseline cost is shown to hold only with respect to the forecast, and directions for extending it to actual conditions are identified.
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References
Beaudin, M., & Zareipour, H. (2015). Home energy management systems: A review of modelling and complexity. Renewable and Sustainable Energy Reviews, 45, 318–335. https://doi.org/10.1016/j.rser.2015.01.046
Han, B., Zahraoui, Y., Mubin, M., Mekhilef, S., Seyedmahmoudian, M., & Stojcevski, A. (2023). Home energy management systems: A review of the concept, architecture, and scheduling strategies. IEEE Access, 11, 19999–20025. https://doi.org/10.1109/ACCESS.2023.3248502
Avizienis, A., Laprie, J.-C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11–33. https://doi.org/10.1109/TDSC.2004.2
Pilz, M., Baghaei Naeini, F., Grammont, K., Smagghe, C., Davis, M., Nebel, J.-C., Al-Fagih, L., & Pfluegel, E. (2020). Security attacks on smart grid scheduling and their defences: A game-theoretic approach. International Journal of Information Security, 19(4), 427–443. https://doi.org/10.1007/s10207-019-00460-z
Sou, K. C., Weimer, J., Sandberg, H., & Johansson, K. H. (2011). Scheduling smart home appliances using mixed integer linear programming. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (pp. 5144–5149). IEEE. https://doi.org/10.1109/CDC.2011.6161081
Liemthong, R., Srithapon, C., Ghosh, P. K., & Chatthaworn, R. (2022). Home energy management strategy-based meta-heuristic optimization for electrical energy cost minimization considering TOU tariffs. Energies, 15(2), 537. https://doi.org/10.3390/en15020537
Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Korab, R., Połomski, M., & Naczyński, T. (2024). Optimal scheduling of energy storage and shiftable loads in grid-connected residential buildings with photovoltaic micro-installations. Energies, 17(21), 5264. https://doi.org/10.3390/en17215264
Cabrera-Tobar, A., Massi Pavan, A., Petrone, G., & Spagnuolo, G. (2022). A review of the optimization and control techniques in the presence of uncertainties for the energy management of microgrids. Energies, 15(23), 9114. https://doi.org/10.3390/en15239114
Tostado-Véliz, M., Arévalo, P., Kamel, S., Zawbaa, H. M., & Jurado, F. (2022). Home energy management system considering effective demand response strategies and uncertainties. Energy Reports, 8, 5256–5271. https://doi.org/10.1016/j.egyr.2022.04.006
Blonsky, M., McKenna, K., Maguire, J., & Vincent, T. (2022). Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods. Applied Energy, 325, 119770. https://doi.org/10.1016/j.apenergy.2022.119770
Silva, C., Faria, P., Vale, Z., & Corchado, J. M. (2022). Demand response performance and uncertainty: A systematic literature review. Energy Strategy Reviews, 41, 100857. https://doi.org/10.1016/j.esr.2022.100857
Yang, D., Wang, W., Gueymard, C. A., Hong, T., Kleissl, J., Huang, J., Perez, M. J., Perez, R., Bright, J. M., Xia, X., van der Meer, D., & Peters, I. M. (2022). A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality. Renewable and Sustainable Energy Reviews, 161, 112348. https://doi.org/10.1016/j.rser.2022.112348
Melnyk, R. V., & Melnyk, M. V. (2025). Metod prohnozuvannia obsiahiv heneratsii enerhii soniachnoiu elektrostantsiieiu [Method for forecasting the energy generation of a solar power plant]. Ukrainskyi zhurnal informatsiinykh tekhnolohii [Ukrainian Journal of Information Technology], 7(1), 149–159. https://doi.org/10.23939/ujit2025.01.149
Kulapin, O. V., & Makhotilo, K. V. (2024). Modeliuvannia hrafikiv navantazhennia prosiumera na bazi povedinkovoho pidkhodu [Modeling of prosumer load schedules based on a behavioral approach]. Enerhetyka: ekonomika, tekhnolohii, ekolohiia [Energy: Economics, Technology, Ecology], (1), 98–105. https://doi.org/10.20535/1813-5420.1.2024.297584
Danylchenko, D. O., Fedorchuk, S. O., Ivakhnov, A. V., Kulapin, O. V., & Hrytsenko, V. V. (2022). Doslidzhennia perspektyv zastosuvannia sotsio-demohrafichnykh danykh dlia analizu potentsialu keruvannia popytom [Study of the prospects of using socio-demographic data for analysing demand response potential]. Visnyk Natsionalnoho tekhnichnoho universytetu «KhPI». Seriia: Enerhetyka: nadiinist ta enerhoefektyvnist [Bulletin of the National Technical University «KhPI». Series: Energy: Reliability and Energy Efficiency], (1(4)), 11–16. https://doi.org/10.20998/2224-0349.2022.01.06
Flores, M., Heredia, D., Andrade, R., & Ibrahim, M. (2022). Smart home IoT network risk assessment using Bayesian networks. Entropy, 24(5), 668. https://doi.org/10.3390/e24050668
Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870–1891. https://doi.org/10.1109/5.192069
Schirmer, P. A., & Mporas, I. (2023). Non-intrusive load monitoring: A review. IEEE Transactions on Smart Grid, 14(1), 769–784. https://doi.org/10.1109/TSG.2022.3189598
Baranski, M., & Voss, J. (2004). Genetic algorithm for pattern detection in NIALM systems. In 2004 IEEE International Conference on Systems, Man and Cybernetics (Vol. 4, pp. 3462–3468). IEEE. https://doi.org/10.1109/ICSMC.2004.1400878
Egarter, D., Sobe, A., & Elmenreich, W. (2013). Evolving non-intrusive load monitoring. In Applications of Evolutionary Computation (pp. 182–191). Springer. https://doi.org/10.1007/978-3-642-37192-9_19
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 311–338. https://doi.org/10.1016/S0045-7825(99)00389-8
Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.7970649
Loutzenhiser, P. G., Manz, H., Felsmann, C., Strachan, P. A., Frank, T., & Maxwell, G. M. (2007). Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation. Solar Energy, 81(2), 254–267. https://doi.org/10.1016/j.solener.2006.03.009
Anderson, K. S., Hansen, C. W., Holmgren, W. F., Jensen, A. R., Mikofski, M. A., & Driesse, A. (2023). pvlib python: 2023 project update. Journal of Open Source Software, 8(92), 5994. https://doi.org/10.21105/joss.05994
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049. https://doi.org/10.1002/qj.3803
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