Description:Field of Invention:
The field of invention for "Solar Prediction for Reliable DC Micro-grid Energy
Supply" falls under renewable energy forecasting, smart grid management, and
AI driven energy optimization. This invention focuses on leveraging advanced
predictive algorithms, including machine learning and weather modelling, to
accurately forecast solar insolation in photovoltaic (PV)based DC micro-grids. By integrating real time environmental data, historical solar patterns, and
intelligent energy management strategies, the system ensures uninterrupted
power supply for critical loads such as medical facilities, communication systems, and industrial automation. The innovation enhances grid resilience, optimizes
battery storage utilization, and reduces reliance on backup power sources, ultimately improving the efficiency and reliability of renewable energy based
micro-grids. Background of the invention:
The increasing reliance on photovoltaic (PV) systems in DC micro-grids for
powering critical loads necessitates accurate solar insolation prediction to ensure
uninterrupted energy supply. Variability in solar irradiance due to changing
weather conditions and seasonal patterns poses challenges in maintaining a stable
energy flow. To address this, advanced forecasting models leveraging artificial
intelligence (AI), machine learning (ML), and meteorological data are being
developed to predict solar insolation with high precision. By integrating realtime
data analytics and optimization algorithms, these models enhance energy
management, enabling efficient load balancing and battery storage utilization. This innovation ensures the ceaseless power supply for critical applications, reducing dependence on conventional grid sources while improving microgrid
resilience and sustainability.
OBJECTIVE OF INVENTION
 The objective of this invention is to develop an intelligent prediction
model for solar insolation in a PV based DC microgrid to ensure a
continuous and reliable energy supply for critical loads.  It integrating advanced forecasting techniques, such as machine learning
and real time meteorological data analysis, the system will accurately
predict solar energy availability, enabling efficient energy management.  This proactive approach will optimize battery storage utilization, enhance
load balancing, and minimize dependency on auxiliary power sources, ensuring an uninterrupted power supply for critical applications in
healthcare, industrial automation, and emergency systems. STATEMENT OF INVENTION:
The invention presents a predictive solar insolation model for a PV based DC
micro-grid, ensuring uninterrupted power supply to critical loads. By integrating
advanced machine learning algorithms with real-time meteorological and
historical solar data, the system accurately forecasts solar energy availability. This predictive approach optimizes energy storage and distribution, enhancing
micro-grid resilience and efficiency. The innovation further includes an
intelligent energy management system that dynamically adjusts power flow
based on predicted insolation, minimizing reliance on backup sources and
reducing operational costs. This solution is crucial for sustaining critical loads
in sectors like healthcare, telecommunications, and emergency services. BRIEF SUMMARY OF THE INVENTION
The invention presents a predictive solar insolation model for a PV-based DC
micro-grid to ensure continuous energy supply for critical loads. By integrating
advanced forecasting techniques, including machine learning (50%) and realtime weather monitoring (30%), the system optimizes energy generation,
storage, and distribution (20%). The predictive model enhances micro-grid
efficiency by anticipating solar energy variations, dynamically adjusting battery
charging, and ensuring uninterrupted power for essential applications. This
innovation significantly improves reliability, minimizes energy wastage, and
reduces dependency on auxiliary power sources, making it ideal for mission
critical operations. DETAILED DESCRIPTION OF COMPONENTS:
SOLAR PANEL
Working Principle:
A solar panel is a device that converts sunlight into electricity using
photovoltaic (PV) cells. These cells absorb solar energy and generate direct
current (DC) electricity through the photovoltaic effect. The generated DC
power can be used directly in DC systems or converted to alternating current
(AC) using an inverter for general applications. Power Output Ranges:
Small scale panels: 5W to 50W (used for small devices, solar lights). Residential panels: 100W to 400W (commonly used for homes and businesses). Industrial/commercial panels: 400W to 700W+ (used in large scale power plants
and micro-grids). Applications and Benefits:
 Used in homes, commercial buildings, micro-grids, and off grid power
solutions.  Reduces carbon footprint and electricity costs.  Provides renewable, sustainable energy with minimal maintenance.  Supports energy storage systems for uninterrupted power supply.
BOOST CONVERTER
1. Definition & Functionality
A boost converter is a DCDC power converter that steps up (increases) the
input voltage to a higher output voltage while regulating the power efficiently. It
operates using an inductor, switch (MOSFET), diode, and capacitor, utilizing
energy storage and release cycles to achieve voltage conversion. 2. Voltage Conversion Range
 Input Voltage: Typically ranges from 2V to 48V depending on the
application.  Output Voltage: Can be boosted up to 5V to 400V, depending on design
requirements and component ratings. 3. Efficiency & Power Handling
 Boost converters generally have an efficiency of 85% to 98%, depending
on the quality of components and control algorithms.  Power handling capability ranges from a few watts (small electronic
circuits) to several kilowatts (industrial and renewable energy
applications). 4. Applications
 Used in solar power systems to step up panel voltage for battery charging.  Found in electric vehicles to boost battery voltage for motor drives.  Essential in portable electronic devices and power management systems
requiring stable high voltage output.
BATTERY
1. Composition & Working Principle
 Lithium ion (Li) batteries use lithium ions moving between the anode
(usually graphite) and the cathode (commonly lithium cobalt oxide, lithium iron phosphate, or lithium nickel manganese cobalt oxide).  They offer high energy density, low self discharge, and long cycle life, making them ideal for portable electronics, electric vehicles, and energy
storage systems. 2. Voltage Ranges
 Nominal Voltage: 3.2V to 3.7V per cell (depending on chemistry).  Charging Voltage: 4.1V to 4.2V per cell.  Discharge Cut off Voltage: 2.5V to 3.0V per cell. 3. Capacity & Energy Density
 Capacity Range: 500mAh to over 100Ah per cell, depending on
application.  Energy Density: 150–300 Wh/kg, with advanced variants reaching up to
400 Wh/kg. 4. Cycle Life & Operating Temperature
 Cycle Life: Typically 500 to 3000 charge cycles, depending on depth of
discharge (DoD) and operating conditions.  Operating Temperature:
 Charging: 0°C to 45°C
 Discharging: 20°C to 60°C (varies by chemistry). CONTROLLER
1. Overview & Functionality
Arduino is an open source microcontroller platform designed for embedded
system applications. It features digital and analog I/O pins, supports multiple
communication protocols (I2C, SPI, UART), and is compatible with sensors, motors, and communication modules for automation and IoT applications.
2. Voltage & Current Ranges
 Operating Voltage: 3.3V to 5V (depending on the board)
 Input Voltage Range: 6V to 20V (recommended 7V to 12V)
 Maximum Current per I/O Pin: 20mA (recommended), 40mA (absolute
max)
 Total DC Current for all I/O: 200mA
3. Processing Speed & Memory
 Clock Speed: 8 MHz to 16 MHz (depending on the model)
 Flash Memory: 2KB to 256KB
 SRAM: 128B to 8KB
 EEPROM: 512B to 4KB
LDR
1. Working Principle:
An LDR (Light Dependent Resistor) is a passive electronic component whose
resistance decreases as the intensity of light falling on it increases. It operates
based on the photoconductivity principle, where photons excite electrons, reducing resistance. 2. Material Composition:
Typically made from cadmium sulfide (CdS) or other photoconductive
materials, LDRs have a high resistance in darkness and low resistance in bright
light. 3. Resistance Range:
 Dark Conditions: High resistance (1MΩ to 10MΩ or more).  Bright Light: Low resistance (100Ω to 1kΩ).
 Moderate Light: Resistance varies between a few kΩ to hundreds of kΩ, depending on the intensity. 4. Applications:
Used in automatic street lights, solar trackers, camera exposure controls, and
smart lighting systems to adjust illumination based on ambient light levels. LCD
1. Working Principle
An LCD (Liquid Crystal Display) operates using liquid crystal molecules
sandwiched between two electrodes and polarizing filters. When an electric
current passes through, the liquid crystals align to control light passage, creating visible images. It is widely used in electronic displays due to its low
power consumption and high clarity. 2. Types of LCDs
 Character LCDs (Alphanumeric): 16x2, 20x4, etc., used for displaying
text (e.g., Arduino projects).  Graphical LCDs: 128x64, 240x128, etc., used for graphical
representations.  TFT LCDs: Highresolution, color displays found in smartphones, tablets, and industrial systems.  OLED and AMOLED: Advanced LCD variants offering better contrast
and flexibility. 3. Common Operating Ranges
 Voltage:Typically, 3V to 5V for small LCDs, while industrial LCDs
may require 12V to 24V.  Temperature Range: Usually 20°C to 70°C, but industrial LCDs can
withstand 40°C to 85°C.
 Viewing Angle: Basic LCDs (~45°), advanced IPS panels (~178°).  Response Time: Standard LCDs (~1025ms), gaming or highspeed
displays (~15ms). 4. Advantages and Applications
 Low Power Consumption: Ideal for battery operated devices.  Wide Use Cases: Found in consumer electronics, medical devices, industrial panels, automotive displays, and IoT projects.  Customizability: Available in monochrome, color, touch, and nontouch
variations for different applications. , Claims:1. The prediction of solar insolation in a PV based dc micro grid to
meet the ceaseless energy demand of critical loads consists of
Solar Insolation Prediction System
 Utilizes machine learning algorithms (e.g., ANN, LSTM, or
regression models) and real time weather data (irradiance, temperature, humidity) to forecast solar energy availability
accurately.  Enhances energy planning by predicting fluctuations and
optimizing PV generation. Energy Storage and Management
 Includes battery energy storage systems (BESS) to store excess
solar energy and ensure continuous power supply to critical loads
during low insolation periods.  Implements intelligent charge discharge control to maximize
battery lifespan and efficiency. Load Demand and Power Distribution Control
 Uses smart energy management systems (EMS) to dynamically
allocate power based on priority, ensuring critical loads receive
uninterrupted energy.  Integrates DCDC converters and MPPT controllers to regulate and
stabilize power flow efficiently. 2. The method of claim, I may further comprise of the following
components
 Solar panel
 Battery
 Boost converter
 Lcd
 Ldr
 Arduino controller
3. As claimed in claim 1,Solar Panel Converts sunlight into electrical
energy to power the system. 4. As claimed in claim2, Battery Stores excess solar energy for use
during low or no sunlight conditions. 5. As claimed in claim 3, Boost Converter Increases the voltage from
the solar panel to the required level for efficient operation. 6. As claimed in claim 4, LCD Displays real time system parameters
such as voltage, current, and battery status. 7. As claimed in claim 5, LDR (Light Dependent Resistor)Detects
ambient light intensity to optimize solar panel positioning or control
lighting systems. 8. As claimed in claim 6, Arduino Controller: Acts as the central
processing unit, managing sensor data, controlling power flow, and
ensuring system automation.