Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm

Abstract

A remodeled sperm swarm optimization (RMSSO) algorithm for a home energy management (HEM) system is proposed, and its real-time efficacy was evaluated using a hardware experimental model. This home environment comprised sixteen residential loads, a smart meter and a Raspberry Pi controller to optimize the energy consumption cost (ECC) in response to the Indian day-ahead pricing (DAP) scheme. A wired/wireless communication network was considered to communicate with the smart meter and controller. To address this optimization problem, the sperm swarm optimization (SSO) algorithm’s constriction coefficient was remodeled to improve its global searching capability and proposed as RMSSO. For the first time, salp swarm optimization (SSA), SSO, and RMSSO algorithms were employed to schedule home appliances in the Indian scenario. To validate the proposed technique’s outcome, the results were compared to those of the conventional SSO and SSA algorithms. This problem was solved using the Python/GUROBI optimizer tool. As a consequence, consumers can use this control strategy in real-time to reduce energy consumption costs.

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

“…According to Equation (3), the group of appliances that are taken into account for scheduling is designated as G and includes both schedulable and non-schedulable equipment [83]:…”

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

“…With the implementation of AC DLC in developing regions, energy, water, and climate challenges were addressed, promoting sustainable energy systems and grid flexibility in spite of the low AC penetration rate. Senthil Prabu Ramalingam et al suggested an RMSSO algorithm with better global searching capabilities than the SSO and SSA algorithms for optimizing energy consumption expenses in a home energy management system (HEM) [23]. RMSSO effectively scheduled residential appliances, demonstrating superior load scheduling and computation time while not taking renewable energy sources into account.…”

Laundry Cluster Management Using Cloud

“…These devices can be based on less advanced controllers such as NodeMCU [7], [8], ESP [9], [10], Raspberry Pi [11], as well as more advanced modules like PLC controllers [12]. Such solutions can utilize artificial intelligence algorithms for efficient management of electricity consumption [13], [14] and [15], or heating systems [16], [17], [18], not only in individual buildings but also in a cluster of buildings managed from the cloud [19], [20] and [21]. Due to the availability of components and a substantial base of online tutorials, it is possible to build custom Smart Home solutions using popular microcontrollers such as Arduino, ESP, etc.…”