Author ORCID Identifier

0000-0003-3450-0735

Date of Award

10-11-2025

Document Type

Thesis

School

School of Electrical & Electroncis Engineering

Programme

Ph.D.-Doctoral of Philosophy

First Advisor

Dr.G.Balasubramanian

Keywords

Home Energy Management System, Smart Grid, Demand Side Management, Load Scheduling, Soft Computing Techniques

Abstract

The rising global energy demand, combined with environmental concerns and the depletion of conventional energy sources, has driven the need for the development of smarter and more sustainable energy systems. This research focuses on Home Energy Management System (HEMS) using soft computing techniques to optimize load scheduling, enhance energy efficiency, and integrate Renewable Energy Sources (RES) such as solar and battery storage. The primary goal is to balance supply and demand, reduce peak loads, and minimize energy costs while ensuring consumer comfort and system reliability.

Smart grids provide enhanced capabilities compared to conventional grids by enabling bidirectional flows of energy and information, real-time monitoring, demand response and integration of distributed energy resources. This research explores key Smart Grid technologies including Advanced Metering Infrastructure (AMI), grid automation, renewable energy integration, and demand-side management (DSM). The research focus is on load scheduling within the HEMS framework to shift consumer energy consumption from peak to off-peak periods. This load shifting contributes to grid stability, lower energy costs and reduced carbon emissions.

The research proposes a fuzzy logic-based intelligent scheduling algorithm that uses real-time input parameters such as photovoltaic (PV) output, battery state of charge (SoC), and power demand. The fuzzy controller evaluates the Probability of Scheduling (POS) based on these inputs and determines whether to operate specific loads. The loads are categorized as base, deferrable, or non-deferrable loads. The fuzzy logic controller is implemented and validated using both simulation (MATLAB Simscape) and real-time experimental setups, including local (Arduino) and remote (ESP32) configurations. Simulation results demonstrate effective load switching based on available power and demand variations, proving the system's practical viability for single home.

For multi-home environments, Artificial Neural Networks (ANN), ANN fitting and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are developed and tested to support large-scale demand response programs. These techniques provide enhanced scalability and learning abilities, facilitating real-time optimization in complex and dynamic environments. ANN and ANN fitting is used for learning consumer patterns, while ANFIS combines the adaptability of ANN with the humanlike reasoning of fuzzy logic for enhanced decision-making.

In conclusion, the proposed fuzzy logic based Mimic-HEMS (M-HEMS) provides a robust platform for DSM in Smart Grids, especially when extended to multi-home environments through ANN, ANN fitting and ANFIS models. The findings highlight the potential of intelligent control strategies to transform traditional energy consumption patterns, optimize renewable energy integration and pave the way toward a sustainable energy future.

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