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Proceedings ArticleDOI

Scheduling and Energy Management of Smart Homes Using Customer Choice Based Algorithm

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TLDR
The proposed technique has effectively managed the peak demand and reduces the cost of energy consumption and is named as Customer Choice Based Algorithm (CCBA).
Abstract
In developing countries, reduction of energy consumption cost, load forecasting, scheduling and control on the utility side are the major concern towards energy management. In this paper, a solution to this problem is addressed using PERT (Program Evaluation and Review Technique) combined with CPM (Critical Path Method). It is a technique to obtain the best solutions for optimized scheduling of household appliances for Home Energy Management System (HEMS). The optimization is carried out with various constraints in the selected home environment. The state of this condition is considered as a Mixed Integer Linear Programming (MILP) problem with more complexity. To reduce the complexity of this problem, it is solved in two stages namely, a grouping of appliances and selection of optimized group. The simulation of scheduling the appliances with all necessary inter dependencies is carried out using the proposed technique which is named as Customer Choice Based Algorithm (CCBA). The input data for simulation are considered from the existing methods and its results and performance are compared with the proposed algorithm. The proposed technique has effectively managed the peak demand and reduces the cost of energy consumption.

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Book ChapterDOI

A Home Energy Management System with Peak Demand Reduction Using Ant Colony Optimization and Time of Use Pricing Scheme

TL;DR: In this article, the authors proposed an optimized home energy management system using Ant Colony Optimization (ACO) algorithm that reduces the energy consumption cost and peak demand by increasing the user comfort (UC) level.
Journal ArticleDOI

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

TL;DR: 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.
Proceedings ArticleDOI

Optimal Battery Scheduling with and without Renewable Energy Sources for Efficient Home Energy Management

TL;DR: In this article , the authors proposed a day-ahead scheduling strategy for a battery in a smart home using two meta-heuristic evolutionary algorithms; antlion optimization (ALO) and salp swarm optimization (SSA) algorithms.
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Investigation on Optimization Algorithms for Smart Home Energy Management with Different Electricity Pricing

TL;DR: In this article , the authors presented a functional and adaptable home energy management system with RES and an energy storage device for designing and implementing Demand Response (DR) programs, where four meta-heuristic techniques: GA, WDO, GWO, and Salp Swarm Optimization (SSA) were used to optimize the energy consumption cost for a home energy environment.
References
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Book

Introduction to Operations Research

TL;DR: The Simplex Method duality theory and sensitivity analysis for linear programming has been studied extensively in the field of operations research as mentioned in this paper, including the application of queueing theory inventory theory forecasting Markovian decision processes and applications decision analysis simulation.
Journal ArticleDOI

An Algorithm for Intelligent Home Energy Management and Demand Response Analysis

TL;DR: An intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis is presented and a simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level.
Journal ArticleDOI

Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization

TL;DR: In this paper, the authors investigated the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 hours and achieved near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem.
Journal ArticleDOI

An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes

TL;DR: The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques.
Journal ArticleDOI

Efficient and Autonomous Energy Management Techniques for the Future Smart Homes

TL;DR: An optimization problem under various practical constraints is formed, which is shown to be a mixed integer programming problem that can be solved through a step-wise approach and a novel scheme based on Dijkstra algorithm is proposed, which results in the similar performance to that of the proposed optimal scheme while exhibiting much lower complexity.
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