Consumer Power Monitoring and Controlling System to Save Power According to the Demand

Abstract

This project develops an intelligent consumer power monitoring and controlling system to optimize energy consumption based on real-time demand. The system utilizes a combination of smart sensors, a microcontroller unit, and a user-friendly interface to monitor appliance energy usage, predict peak demand periods, and automatically adjust power consumption to minimize costs and reduce carbon footprint. The system's effectiveness is evaluated through simulated and real-world testing, demonstrating significant energy savings compared to existing methods. The project contributes to the growing field of smart home energy management by offering a cost-effective and user-friendly solution for individual consumers.

Introduction

Rising energy costs and environmental concerns necessitate the development of efficient energy management solutions. While smart grids and utility-level demand response programs exist, effective individual-level control remains a challenge. Current solutions often lack user-friendliness, real-time adaptability, or granular control over individual appliances. This project addresses this gap by creating a consumer-centric system capable of learning individual energy consumption patterns and optimizing power usage based on dynamic demand and pricing. The challenges include developing accurate energy prediction models, designing a robust and reliable system, and ensuring user acceptance and ease of use.

Objectives

• Develop a system capable of accurately monitoring energy consumption of multiple household appliances.
• Implement an algorithm to predict peak energy demand and automatically adjust appliance power based on demand and user preferences.
• Create a user-friendly interface for monitoring energy usage and system configuration.

Technical Details

• Smart sensors for real-time energy usage tracking.
• Microcontroller (Arduino/Raspberry Pi) for processing and control.
• Power control relays for switching appliances based on demand.
• Machine learning algorithm for peak demand prediction.
• Mobile/web-based interface for user monitoring and interaction.