Application Of Gaze Cues Learning-Based Flow Direction Algorithm For Energy Management In Smart Homes

K. Purushotham; J. Sreenivasulu

doi:10.29284/e2j5g227

Authors

K. Purushotham Research Scholar, Department of EEE, Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India.
J. Sreenivasulu Assistant Professor, Department of EEE, Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India.

DOI:

https://doi.org/10.29284/e2j5g227

Keywords:

Gaze Cues Learning, Demand Side Management, Flow Direction Algorithm, Energy Scheduling, Home Energy Management.

Abstract

There is no end to the expansion of automated home appliances driven by the proliferation of cutting-edge technology. To ensure the safety of home installations, it is essential to determine how to regulate our electrical usage. In this regard, Demand Side Management (DSM) is a proposed solution that has been crucial in the development of microgrids and Smart Grids. Through the DSM program, customers can interact with the grid operator, allowing them to participate in decision-making and helping utilities reduce peak power demand. Achieving this goal is possible with careful load management and consistent client satisfaction. Nowadays, several DSM programs have been proposed in the literature, with almost all of them focusing on domestic sector energy management systems. This article proposes an enhanced Flow Direction Algorithm (FDA) that utilizes the gaze cues learning strategy to improve its performance. Gaze cues learning enhances the adaptability of the algorithm, promoting better exploration of potential solutions and improving overall convergence towards the optimal flow direction. The performance of the proposed Gaze Cues Learning Flow Direction Algorithm (GFDA) is evaluated using 14 standard benchmark functions, as well as energy scheduling in smart homes. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical Peak Price and Real-Time Price. Simulation results demonstrate that the proposed model optimally schedules appliances, leading to reduced electricity costs and peak reductions without compromising user comfort. Hence, the results confirm the superiority of the GFDA algorithm over other optimization techniques.

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