concept

smart home appliances

Also known as: smart appliance

Facts (22)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 17 facts

referenceMakhadmeh SN, Khader AT, Al-Betar MA, and Naim S presented research on optimal power scheduling for smart home appliances using a smart battery and the grey wolf optimizer at the 2018 8th IEEE International Conference on Control System, Computing and Engineering.

referenceShuja SM, Javaid N, Khan S, Akmal H, Hanif M, Fazalullah Q, and Khan ZA published 'Efficient scheduling of smart home appliances for energy management by cost and PAR optimization algorithm in smart grid' in 2019.

referenceSou KC, Weimer J, Sandberg H, and Johansson KH utilized mixed integer linear programming to schedule smart home appliances, presented at the 2011 IEEE Conference on Decision and Control and European Control Conference.

claimLoad profiling assessment techniques such as surveys, questionnaires, bottom-up, and top-down approaches are less technically complex, accurate, and time-consuming than using smart appliances and smart meters, but they are significantly more costly to perform.

claimWang et al. (2012) developed an ideal dispatching model for smart Home Energy Management Systems (HEMS) with distributed energy resources and smart home appliances using the mixed integer nonlinear programming (MINLP) methodology, which decreases both electricity costs and total energy usage.

claimSmart appliances facilitate efficient load management systems by utilizing built-in communication sensors that link with smart meters to analyze energy usage based on ambient data and power/tariff parameters.

claimExtensive research is needed to secure the security and privacy of customers’ data for the real-time synchronization and integration of security, safety, smart appliances, and monitoring.

referenceShuja et al. (2019) proposed an efficient scheduling method for smart home appliances using a cost and Peak-to-Average Ratio (PAR) optimization algorithm, published in IEEE Access, 7:102517 (Note: Source text lists journal as IEEE Access, though volume/page data is implied).

claimLoad profiling assessment techniques such as surveys, questionnaires, and bottom-up or top-down approaches are less technically complex and less accurate than using smart appliances and smart meters, and they are also more time-consuming and costly.

claimExtensive research is needed to secure the privacy and security of customer data regarding the real-time synchronization and integration of security, safety, smart appliances, and monitoring systems.

perspectiveUtilizing smart appliances and meters is considered the best option for accurate appliance forecasting, compared to survey techniques, bottom-up models, top-down models, and hybrid methods.

claimSmart appliances are essential for efficient load management systems because they utilize built-in communication sensors to link with smart meters, allowing them to analyze energy usage by collecting ambient data and operating based on provided power and tariff parameters.

claimProedrou (2021) suggests that utilizing smart appliances and meters is the optimal approach for accurate appliance forecasting, despite the exploration of survey techniques, bottom-up models, top-down models, and hybrid methods.

claimThe implementation of demand-side management (DSM) requires planning and managing decision parameters and operating constraints, which depend on factors such as appliance load profiles, renewable energy integration, load categorization, dynamic pricing, consumer categorization, optimization techniques, consumer behaviors, electricity data issues, knowledge availability, framework reliability, smart/grid-intelligent appliances, and control strategies.

claimDemand Side Management (DSM) can provide an optimal management system by scheduling smart appliances and integrating renewable energy sources (RES) such as solar, wind, distributed micro-generators, and energy storage devices like plug-in electric automobiles and batteries (Qureshi et al. 2021; Wang et al. 2019; Wu et al. 2019).

referenceEffective Demand Side Management (DSM) systems can integrate smart appliances, renewable energy sources (solar, wind, micro-generators), and energy storage devices (plug-in electric vehicles and batteries) to optimize management (Qureshi et al. 2021; Wang et al. 2019; Wu et al. 2019).

referenceMakhadmeh SN, Khader AT, Al-Betar MA, and Naim S proposed an optimal power scheduling method for smart home appliances using a smart battery and the grey wolf optimizer in 2018.

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 4 facts

claimCommunicating thermostats and smart appliances connected via Home Area Networks (HANs) allow for dynamic adjustments to energy-intensive systems based on pricing signals or predefined schedules.

claimSmart appliances and thermostats enhance consumer comfort and convenience while aligning energy consumption with cost-saving opportunities.

claimCommunicating thermostats and smart appliances connected via Home Area Networks (HANs) allow for dynamic adjustments to energy-intensive systems based on pricing signals or predefined schedules.

claimCommunicating thermostats and smart appliances connected via Home Area Networks (HANs) allow for dynamic adjustments to heating, cooling, and other energy-intensive systems based on pricing signals or predefined schedules.

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 1 fact

referenceThe GreenTech Nexus system includes smart meters for real-time energy tracking, user interfaces for interactive management, scheduler modules for energy allocation, Home Energy Management (HEM) systems, smart appliances, photovoltaic (PV) panels, Battery Energy Storage Systems (BESS), and electric vehicles (EVs) that function as both loads and energy reservoirs.