Engineering: Development and Performance Evaluation of an AI-Enabled Smart Energy Management System for Sustainable Buildings

Engineering: Development and Performance Evaluation of an AI-Enabled Smart Energy Manageme.

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Original Research | 2026 | Volume 1 | Issue 2 | Page 124-138

Dr. Narendra Sharma, Lecturer, Department of Anatomy, Dr. Rajendra Gode Medical College & Hospital, Amravati, Maharashtra, Phone No.- 91-8287594970, E-Mail ID -Narendra120892@Gmail.Com.

Abstract

The increasing demand for energy-efficient infrastructure and the global emphasis on

sustainable development have accelerated the adoption of intelligent building management

technologies. Artificial intelligence (AI)-enabled Smart Energy Management Systems

(SEMS) have emerged as an effective solution for optimizing energy consumption, reducing

operational costs, and minimizing carbon emissions in residential, commercial, and

institutional buildings. This study presents the development and performance evaluation of an

AI-enabled Smart Energy Management System designed to enhance energy efficiency

through real-time monitoring, predictive analytics, and automated control strategies. The

proposed system integrates Internet of Things (IoT)-based sensors, smart meters,

environmental monitoring devices, and machine learning algorithms to continuously analyze

occupancy patterns, weather conditions, appliance usage, and electricity demand. A

predictive optimization model was implemented to regulate lighting, heating, ventilation, air

conditioning (HVAC), and electrical loads while maintaining occupant comfort. System

performance was evaluated using key indicators including total energy consumption, peak

load reduction, operational cost savings, response time, prediction accuracy, and carbon

emission reduction. Experimental evaluation demonstrated significant improvements in

overall energy efficiency, with reductions in electricity consumption, peak demand, and

greenhouse gas emissions compared with conventional rule-based energy management

approaches. The AI-based predictive model achieved high forecasting accuracy, enabling

proactive energy scheduling and adaptive control under varying environmental conditions.

User comfort and system reliability were maintained throughout the evaluation period. The

findings indicate that integrating AI, IoT, and intelligent automation provides a scalable and

cost-effective framework for sustainable building operation. The proposed Smart Energy

Management System offers substantial potential for supporting green building initiatives,

smart city development, and national energy conservation objectives while contributing to

long-term environmental sustainability and resilient urban infrastructure.