Design of Demand Side Management (DSM) Technology-based Domestic Lighting

Abstract

Introduction: This article explores the implementation of demand-side management in domestic lighting through the Internet of Things (IoT). The objective is to enhance energy efficiency by dynamically controlling lighting systems based on real-time demand and user preferences. The study involves the design and deployment of IoT-enabled devices to monitor, analyze, and optimize lighting usage, contributing to a more sustainable and cost-effective residential energy consumption model. Key aspects include sensor integration, data analytics, and user interface development for seamless control and monitoring. The findings highlight the potential for significant energy savings and improved user experience in domestic lighting through IoT-based demand site management.

.Objectives: To design a physical usable prototype to demonstrate how a simple DSM technology can be achieved at home.

Methods: The proposed work aims to design and implement a prototype model that demonstrates Demand Side Management (DSM) in domestic lighting using Internet of Things (IoT) technology. The methodology adopted in this study consists of the following steps: (i) Selection of IoT Control Platform: A Wi-Fi enabled microcontroller board, such as ESP32 or NodeMCU, is selected to serve as the primary control unit. The inbuilt Wi-Fi capability ensures seamless connectivity with cloud servers and mobile devices for DSM implementation. (ii) Programming of Control Logic: The control board is programmed using the Arduino IDE with customized codes that define the DSM strategies. The program enables intelligent switching, load scheduling, and communication with the cloud server. (iii) Circuit Design and Prototype Development: An electronic circuit is designed to replicate domestic lighting loads. Light-Emitting Diodes (LEDs) are used to simulate household lighting, while relays are employed to represent actual switching devices. The control board, sensors, and actuators are integrated to develop a functional prototype. (iv) Simulation and Verification: The designed circuit and control logic are first tested using simulation tools such as Proteus or Thinkercad. Simulation helps verify circuit operation, connectivity, and DSM algorithm performance before physical implementation. (v) Hardware Implementation: Upon successful simulation, the prototype is realized at the hardware level using physical components. This step demonstrates the feasibility of the DSM-based lighting system in a real-time environment. (vi) Cloud Integration and Remote Monitoring: A cloud server (such as ThingSpeak, Firebase, or Blynk) is configured to host the IoT program. This enables remote monitoring and control of lighting loads via a web or mobile interface, thereby validating the DSM functionality.  

Results: After reviewing and thoroughly testing the prototype, the results obtained were positive. The sample system performed reliably, and it was observed that the design can be scaled up depending on the capacity of the chosen control board.

The incorporation of luminosity sensors proved effective in detecting excess ambient light inside the house. This provided users with actionable information, helping them reduce unnecessary use of artificial lighting when natural light was sufficient. Similarly, the home energy monitoring system enabled real-time tracking of energy consumption. Unlike conventional household meter boxes, which are often ignored, the integration of energy data into a smartphone interface ensured that users could conveniently monitor and respond to their consumption patterns.

Overall, the prototype successfully demonstrated the ability of IoT and sensor-based systems to minimize energy wastage caused by user negligence in home lighting, while also promoting awareness of energy use.

Conclusions: This project has demonstrated the feasibility and effectiveness of applying Internet of Things (IoT) technology to demand-side management in domestic lighting. By integrating sensors, control algorithms, and cloud connectivity, the system dynamically adjusted lighting conditions based on real-time demand and user behavior. The results confirmed notable improvements in energy efficiency and user awareness, showing that IoT-based home lighting systems can reduce wastage and contribute to sustainable energy use. The combination of a luminosity sensor for ambient light detection and an energy monitoring system accessible via smartphone enhances both convenience and responsibility in household energy management. The successful implementation of this prototype highlights its potential scalability and broader application in modern households. Future work should focus on refining the design, improving user interfaces, and exploring large-scale deployment. This study represents a meaningful step toward creating smarter, energy-conscious homes through the fusion of IoT and DSM strategies.