SMART LIGHTING MODE SYSTEM FOR ENERGY-EFFICIENT BUILDING MANAGEMENT

Abstract

ABSTRACT This project introduces an intelligent sun light intensity display system. By utilizing LDR sensors, real time sun light data is collected and analyzed through advanced algorithms integrated in to a central control system. This enables dynamic adjustments based on time, season and specific activities such as sleeping, living and reading modes. The project employs an Arduino UNO micro controller and a 16 X 2 LCD module for data presentation, enhancing user experience which sustain human eye health, energy efficiency for existing buildings and also suggesting window position for new buildings.

Specification

Description:Technical Field:
This invention relates to the field of energy-efficient building management systems, specifically for availability to middle and lower middle-class people that optimizes energy usage by manually and will make awareness of increase or decrease the light intensity to adjusting lighting conditions based on various environmental and operational parameters. This will increase the safety of eye health of the students or readers and also optimizing the energy.
Background of the Invention:
In India, around 50% of the students having reading glasses because of poor vision. Proper lighting system is very important for not damaging eye sight. Reading in low lighting is one of the reasons for damage of human eye health. On the other side, in India, 90% people from middle class who concern about electricity bill in provision of proper lighting system.
In this scenario, the building lighting mode system is developed to facilitate to identify the lighting in the room and suggest the person, the type of the lighting system based on the requirement.
Summary of the Invention:
The present invention provides a Smart Lighting Mode System that manually manages building illumination based on a combination of environmental factors such as ambient light, occupancy, time of day, and user-defined preferences. This system is designed to reduce energy consumption and damage of eye health of students or readers while maintaining optimal lighting conditions for users.
The system employs a network of sensors that detect ambient light levels, occupancy, and movement in specific zones of the building. These sensors, in conjunction with a central processing unit (CPU), analyze real-time data and adjust lighting intensity and mode (e.g., dimming, brightening, or turning off lights) accordingly. The system also factors in external environmental conditions, such as daylight availability, by integrating with building management systems (BMS) or external weather monitoring services.
The key feature of the system is its adaptive lighting modes that are pre-configured based on different usage scenarios. For instance:
Reading Mode: Optimizes the use of natural daylight to minimize artificial lighting during the daytime. 300-500 lux is typical for reading or office work.
Living mode: Lighting intensity during periods of low activity or when natural light is sufficient. Typically, around 100 to 300 lux, which is softer than task lighting and creates a more relaxed environment.
Night Mode: Ensures sufficient lighting during nighttime with low-intensity, energy-saving settings. Less than 100 lux considered as Night mode.
In addition, the system can be controlled and monitored remotely via a smartphone application or web interface, providing users with the flexibility to manage lighting from any location.
Detailed Description of the Invention: The Smart Lighting Mode System consists of the following primary components:
Sensor Network: A distributed array of sensors installed throughout the building to detect occupancy, ambient light levels, motion, and time of day. These sensors communicate wirelessly or via wired connections with a central control unit.
Central Control Unit (CCU): A processing hub that collects and analyzes sensor data in real-time. The CCU determines the optimal lighting mode based on pre-configured settings and inputs from sensors. The unit includes an embedded algorithm that continuously learns from environmental data to improve energy efficiency over time.
Smart Light Fixtures: These fixtures are connected to the central system and can adjust their brightness, color temperature, or power state (on/off) as per the instructions from the control unit. They can operate in different modes such as dimmed, full brightness, or power-off when no occupancy is detected.
User Interface (UI): A user-friendly interface accessible via mobile apps or web platforms allows users to configure lighting modes, set schedules, or override the system's automatic controls if necessary. The interface also provides real-time energy usage data and savings reports.
Machine Learning Algorithm: The system integrates a machine learning-based algorithm that refines the lighting control strategies over time. It identifies patterns in building occupancy, daylight variation, and energy consumption, and autonomously adjusts the system to maximize energy savings without sacrificing comfort.
Components used
1. Arduino UNO
2. 16×2 LCD
3. EL 3021
4. 4N25
5. ON-OFF Switch
6. BT136
7. 12-0, 1 Amp Transformer
8. Two pin plug.
9. 1N4007 diode
10. Terminal block: PBT-2
11. 6-pin IC base-2 pieces
12. Resistors-10k, 3k3, 330 and 120 ohm
13. Bread Board for connection
Components working principle:
The project controls the AC voltage by sending pulses to the Gate pin of BT136 TRIAC. When the circuit is powered ON, the Arduino sketch initializes the circuit and start reading analog voltage supplied through variable resistance at A0 pin. The zero voltage crossing is detected by the 4N25 circuit. The AC voltage from main supplies is stepped down to 12V AC by the transformer and rectified by the 1N4007 diode full- wave rectifier. The AC voltage once converted to DC voltage drives the IR diode of 4N25 forward biasing it for voltage levels greater than 1.1 Volt.
When the voltage level of the rectified wave is above zero voltage crossing, phototransistor of 4N25 remains in forward biased condition, short-circuiting the VCC supply at pin 2 of Arduino to ground. Therefore, for a majority of the waveform, a LOW logic is received at pin 2 of Arduino. When voltage level approaches zero voltage crossing, the IR diode of 4N25 does not get the required voltage for forward biasing. So, the phototransistor of 4N25 switches to unbiased condition and the pin 2 of Arduino get a HIGH pulse upon zero voltage crossing.
The Arduino detects the zero-voltage crossing and determines a firing angle based on the voltage supplied through variable resistance at pin A0. The analog voltage at A0 pin is read by the Arduino and converted to a digital reading using in-built ADC channel. A time interval based on digitized voltage reading is calculated in the Arduino Sketch. An interrupt routine shooting a HIGH pulse for 50 microseconds after the delay of the resultant time interval is activated at pin 10 of the Arduino. The HIGH pulse drives the IR diode of an EL3021 optocoupler with the same delay of the calculated time interval. This, in turn, drives the triggering pulse at the Gate terminal of BT136 TRIAC with the same delay equivalent to the calculated time interval.
In power control application using TRIAC, the voltage pulse before the emergence of triggering pulse at Gate terminal of TRIAC gets chopped off while the part of AC voltage wave after the emergence of triggering pulse at Gate terminal of TRIAC remains available for supply to the load.
Check out the Arduino program which is detecting zero voltage crossing based on digital logic at pin 2, calculating a time delay for triggering pulse by digitizing analog voltage through variable resistance at A0 pin and generating a triggering pulse at pin 10 in an interrupt routine. The circuit diagram is shown in Figure 1. And the model is shown in Figure 2.

Advantages:
Energy Efficiency: The system significantly reduces unnecessary energy consumption by dynamically adjusting lighting based on real-time conditions.
User Comfort: Ensures optimal lighting conditions for occupants, taking into account their preferences, tasks, and environmental factors.
Cost Savings: Lower energy usage results in decreased utility bills for building operators and users.
Environmentally Friendly: Contributes to reducing the building's carbon footprint by minimizing energy waste.
Scalability: The system can be easily scaled to small buildings sizes, from small offices to large commercial complexes.
Application:
The sensor-based visible lighting systems in building design showcases a promising solution for comfort for reading, sustainable and smart construction. The project successfully demonstrates how advanced technologies can enhance energy efficiency, occupant comfort, and overall building performance. By striking a balance between artificial lighting and natural light utilization, this integration ensures a harmonious relationship between humans and the built environment.
, Claims:I/We Claim:
Claim 1:
A smart lighting mode system for energy-efficient building management, comprising:

a plurality of ambient light sensors for detecting natural and artificial light levels within various zones of a building;
one or more occupancy sensors for detecting human presence or movement in said zones;
a central control unit operatively connected to said ambient light sensors and said occupancy sensors, wherein the central control unit is configured to:
receive data from said sensors;
process said data to determine optimal lighting conditions for each zone based on real-time environmental conditions and occupancy status;
generate control signals for adjusting the intensity, brightness, or on/off state of lighting fixtures in said zones;
a plurality of smart light fixtures responsive to the control signals from the central control unit, wherein said smart light fixtures adjust their illumination levels according to pre-configured lighting modes, such as day mode, night mode, occupancy mode, energy-saving mode, and custom mode;
a user interface for remotely configuring, monitoring, and managing the lighting modes of the system.
Claim 2:
The smart lighting mode system as claimed in Claim 1, wherein said central control unit comprises a machine learning algorithm configured to autonomously adjust the lighting control strategies over time based on patterns of building occupancy, daylight variation, and energy consumption.
Claim 3:
The smart lighting mode system as claimed in Claim 1, wherein said lighting modes include:
Day Mode, configured to maximize the use of natural daylight and minimize artificial lighting during daylight hours;
Night Mode, configured to provide low-intensity lighting during night hours for safety and security;
Occupancy Mode, configured to switch lighting on when occupancy is detected and off when no presence is detected;
Energy-Saving Mode, configured to dim or reduce lighting intensity when ambient light or low activity is detected;
Custom Mode, configured to allow user-defined lighting settings for specific tasks or environments.
Claim 4:
The smart lighting mode system as claimed in Claim 1, wherein the system is operable via a user interface accessible through a mobile application or web platform, said interface enabling users to:
configure lighting schedules;
override automatic lighting adjustments;
monitor real-time energy consumption and savings statistics.
Claim 5:
The smart lighting mode system as claimed in Claim 1, wherein said central control unit integrates with external building management systems (BMS) or external weather services to adjust lighting modes based on external environmental conditions, such as outdoor daylight levels or weather patterns.

Claim 6:
The smart lighting mode system as claimed in Claim 1, wherein said smart light fixtures are capable of adjusting the color temperature of the emitted light in addition to intensity, based on user preferences or pre-configured settings.
Claim 7:
The smart lighting mode system as claimed in Claim 1, wherein the system is scalable for use in various building environments, including but not limited to residential buildings, office complexes, educational institutions, industrial facilities, and public spaces.
Claim 8:
The smart lighting mode system as claimed in Claim 1, wherein said central control unit is configured to operate in coordination with an energy monitoring subsystem that records and optimizes energy consumption in real-time, providing feedback to users or building operators regarding energy savings and efficiency.
Claim 9:
The smart lighting mode system as claimed in Claim 1, wherein the system includes a fail-safe mode, wherein in the event of sensor or communication failure, lighting fixtures revert to a pre-programmed default mode ensuring safety and adequate illumination.
Claim 10:
The smart lighting mode system as claimed in Claim 1, wherein the system further includes the ability to execute scheduled lighting operations, wherein lights automatically adjust according to pre-determined schedules based on the time of day, calendar events, or user preferences.

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