In-House Energy Consumption Scheduling Optimisation Model.
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| Title: | In-House Energy Consumption Scheduling Optimisation Model. |
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| Authors: | Komasilovs, Vitalijs1 (AUTHOR), Zacepins, Aleksejs1,2 (AUTHOR) aleksejs.zacepins@lbtu.lv, Meitalovs, Jurijs2,3 (AUTHOR), Paura, Liga1,4 (AUTHOR), Stetjuha, Mihails3 (AUTHOR), Varfolomejevs, Andrejs4 (AUTHOR), Salajevs, Vladimirs1 (AUTHOR), Arhipova, Irina1 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 9, p2190. 17p. |
| Subject Terms: | *Constraint programming, *Constraint satisfaction, *Energy consumption, *Mathematical optimization, *Electricity pricing, *Energy management, *Clean energy, *Sensitivity analysis |
| Abstract: | This paper presents an optimisation model for scheduling in-house energy consumption to improve efficiency and sustainability. Focus is on the integration of advanced scheduling techniques to improve the overall performance of the house appliances and energy storage system. The proposed model applies constraint programming and satisfiability (CP-SAT) techniques to analyse complex schedules. A sensitivity analysis was conducted by perturbing key input parameters, including electricity price variations and demand profiles, while tracking output metrics such as total cost, load distribution, and computational performance. The model incorporates real-world constraints, including fluctuating electricity prices and renewable energy availability, to improve efficiency and reduce operational costs. The optimisation of the scheduling task was set for a 36 h time period with time resolutions of 15 min, equal to the electricity price time step. The proposed approach is evaluated through simulation using representative household consumption profiles and real day-ahead electricity prices data. The performance of the proposed CP-SAT model was evaluated, and the model's response to the input parameter change has been analysed. The computational performance and cost outcomes of the proposed CP-SAT approach are comparable to those reported for established HEMS optimisation methods. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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