Application of Predictive Control in Scheduling of Domestic Appliances

Nagpal, Himanshu; Staino, Andrea; Basu, Biswajit

doi:10.3390/app10051627

Cited by 10 publications

(8 citation statements)

References 51 publications

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“…In the first step of the simulation, the actual indoor temperature is considered to be equal to the desired temperature unless the heating or cooling maximal power is exceeded. The amount of heating or cooling power needed to achieve the desired temperature is calculated from the static equation considering the outside temperature and the sunshine power: u(1) = K • (T inside (1) − T outside (1)) − P Sun (1),…”

Section: Heating and Cooling Subsystemmentioning

confidence: 99%

“…Buildings account for 20-40% of the overall energy consumption in developed countries, out of which 50% corresponds to heating ventilation air-conditioning (HVAC) systems energy consumption as stated in [1,2]. As the penetration of renewable energy sources rises, the demands on the power grid are growing as the stabilization of the grid becomes more difficult by incorporating these resources [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House

et al. 2022

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Some analyses state that buildings contribute to overall energy consumption by 20–40%, which, in the context of the recent geopolitical energy crisis, makes them a critical issue to study. Finding solutions for better energy management in buildings can have a significant impact on the energy sector, thus reducing EU energy dependencies and contributing to the fulfillment of the REPowerEU goals. This paper focuses on proposing a simplified model of a residential house considering the main appliances, heating and cooling, a photovoltaic system, and electric vehicle recharging. Weather and solar irradiance forecasts are taken into account. The model predicts the energy demands of a house based on online weather forecasts and the desired indoor temperature. The article also focuses on the analysis of how weather forecast uncertainty affects energy demand prediction. This model can be used to better understand and predict the energy demand of either a single house or a set of houses. A multi-objective optimization approach that takes into account the preferences of users/inhabitants is developed to provide a compromise between the price paid for the electricity and temperature comfort. The authors plan to apply the proposed model to a residential house’s real-time control system. The model will be tuned, its predictions will be tested, and it will be used for energy demand optimization.

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Section: Heating and Cooling Subsystemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House

et al. 2022

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“…On the other hand, robust optimization deals with the price uncertainty intervals [136]. A mixed-integer programming optimization was used for smart home appliance scheduling [137][138][139], with EV and energy storage in [140]. Reference [141] transformed the Mixed-Integer Linear Programming (MILP) problem into a convex programming optimization one for flexible and efficient performance.…”

Section: Optimization Based Dsmmentioning

confidence: 99%

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

et al. 2021

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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

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“…MPC smart home appliance scheduling is mainly concerned with thermostatically regulated loads, such as heating, ventilation and air-conditioning (HVAC) units [27]- [29] and refrigerators [30]. Nonetheless, it has also been recently applied for load coordination of multiple customer microgrids [31], [32].…”

Section: Introductionmentioning

confidence: 99%

“…Two main findings emerge from the literature on household appliance scheduling. First, non-controllable electricity demand is not considered in some works, e.g., [10], [26], [31], which can have a potentially negative infrastructure impact, particularly if carried out at a large scale. This effect results from a finite supply power, which, if surpassed, may damage existing infrastructure and cause power outages.…”

Section: Introductionmentioning

confidence: 99%