Smart Building Energy Management System with IoT-Based Fault Detection

Project Abstract

This study presents the design, implementation, and evaluation of an integrated Smart Building Energy Management System (SBEMS) that leverages Internet of Things (IoT) technology to optimize energy consumption while enabling real-time fault detection across building subsystems. The proposed framework combines a modular sensor network, edge computing devices, and a cloud-based analytics platform to monitor electrical, HVAC, lighting, and renewable energy components. Energy usage data are collected from heterogeneous sensors (current, voltage, occupancy, temperature, humidity, luminance) and harmonized through a standardized data model to support scalable analytics. A hybrid control strategy integrates rule-based optimization with machine learning-driven predictive models to reduce peak demand, minimize energy waste, and maintain occupant comfort within predefined service levels. The IoT layer features low-power microcontrollers, secure wireless communication with encrypted channels, and local fail-safe controllers to ensure uninterrupted operation in case of network disruptions. The fault detection subsystem employs supervised and unsupervised learning techniques, including anomaly detection, diagnostic reasoning, and prognostics, to identify equipment degradation, sensor drift, and abnormal operating conditions. By correlating multisource signals, the system locates faults at equipment, subsystem, or installation levels, provides confidence scores, and triggers automated maintenance escalation. The integration of digital twin concepts enables near real-time simulation of energy flows and fault scenarios to validate control decisions and test maintenance strategies before deployment. The research encompasses data governance, privacy-preserving analytics, and robust cybersecurity measures to mitigate cyber-physical risks. A multi-objective optimization framework balances energy cost, occupant comfort, and system reliability, while a feedback loop ensures continual learning and adaptation to seasonal variations and evolving building usage patterns. The methodology includes a phased deployment in a representative building, followed by controlled experiments comparing SBEMS performance against a baseline energy management approach. Key performance indicators (KPIs) include total annual energy consumption, peak demand reduction, energy cost savings, reduced greenhouse gas emissions, mean time to detection for faults, preventive maintenance interval improvements, and user satisfaction metrics. Results indicate that the SBEMS achieves substantial energy savings through coordinated control of HVAC setpoints, lighting, and demand response participation, while the fault detection subsystem demonstrates high precision and recall in identifying compressor faults, sensor calibration issues, and unmet occupancy-based lighting requirements. The fault diagnostics capability accelerates maintenance planning, reduces unscheduled downtime, and extends asset life by enabling proactive interventions. The study discusses scalability considerations, integration with existing building management systems, and transferability to different climatic zones and building typologies. Finally, the research outlines practical deployment guidelines, performance monitoring dashboards, and recommendations for policy and standards alignment to promote widespread adoption of IoT-enabled energy optimization and fault-aware operations in modern intelligent buildings.

Project Overview

What This Project Is About

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A plain-language overview of the topic and what the project investigates.

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The Problem It Addresses

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What problem or gap this project tackles and why it matters to the field or society.

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Objectives of the Project

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1. Identify ways to monitor energy use in a building efficiently
2. Detect unusual electrical behavior that might indicate faults
3. Suggest simple, automatic actions to save energy without harming comfort
4. Prototype a system that connects sensors and alarms with a central dashboard
5. Evaluate system performance in terms of energy savings and fault detection accuracy

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What You Will Do Step by Step

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1. Review basic concepts of energy use in buildings and fault detection
2. Design a simple architecture with sensors (temperature, power, occupancy) and a gateway
3. Install low-cost sensors and collect daily data from a real or simulated space
4. Develop a basic dashboard to show energy trends and alerts
5. Create simple rules to flag potential faults and suggest actions
6. Test the system under different scenarios and analyze results
7. Document methods, challenges, and improvements for future work

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Expected Outcome

A functioning, easy-to-understand prototype that monitors energy use, flags potential faults, and suggests corrective steps, with a clear demonstration of potential energy savings and reliability improvements.