Energy Optimization in Smart Buildings using Intelligent Fuzzy Ventilation

Naqvi, Furqan; Iqbal, Sohail; Ain, Qurat-ul

doi:10.1109/icecce47252.2019.8940705

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In [1][2][3], the authors focus on providing price signals and financial incentives for home energy management to reduce A2P ratios. In [4][5][6], we can find a model user-friendly and control the process accordingly, reducing the A2P ratio as a by-product. In [7][8][9][10], the authors collect consumer preferences to reduce energy load, consumer input, comfort modeling and preference collection are more suitable for smart home and automation.…”

Section: Introductionmentioning

confidence: 99%

Temporal Preferences-Based Utility Control for Smart Homes

Naseer¹,

Saleem²,

Ghafoor³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy devices make managing this ratio critical, albeit difficult. Various Demand Response (DR) and Demand Side Management (DSM) systems have been proposed to reduce this ratio to 1. The main ways to achieve this are economic incentives, user comfort modeling and control, or preference-based. In this study, we propose a unique opportunistic social time approach called the Time Utility Based Control Feature (TUBCF), which uses the concept of a utility function from economics to model and control consumer devices. We propose a DR model for residential customers to reduce Peak-to-Average Ratio (PAR) and improve customer satisfaction by eliminating Appliance Wait Time (WTA) during peak periods. For PAR reduction and WTA, we propose a system architecture and mathematical formulation. Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics. Simulation results show that using this strategy can reduce A2P by 80% and improve user comfort during peak hours.

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