BMP180 – HVS Technologies

HVS-4958. Machine Learning based Wind Energy Forecasting for Energy Management in microgrid System

hvsadmin — Wed, 17 Jun 2026 11:49:31 +0000

The increasing demand for renewable energy integration into microgrid systems highlights the need for accurate forecasting and efficient energy management strategies. This project presents a Machine Learning-based wind energy forecasting and monitoring system for microgrid applications. Wind energy is captured and stored in a battery through a charging circuit and converted to usable AC power via an inverter. An Arduino UNO collects real-time voltage data from a voltage sensor, while a BMP180 sensor measures altitude, temperature, and pressure. These data streams are transmitted to a Raspberry Pi Zero 2W, which functions as the central processing and communication unit. The Raspberry Pi stores and visualizes the sensor data on a web-based platform, while also providing a real-time display on an LCD module. A machine learning model processes the historical and real-time data to forecast wind energy generation, thereby enabling improved reliability, optimized energy management, and effective load balancing in microgrid systems. This integrated approach ensures sustainable and resilient operation of hybrid renewable energy infrastructures.

The main objectives of the project are:

To monitor wind energy generation in real time using sensors.
To measure altitude, temperature, and pressure with the BMP180 sensor.
To display sensor data locally on an LCD and web page.
To apply machine learning techniques for wind energy forecasting.

The major building blocks of this project are:

Power Supply.
Raspberry pi Zero 2w.
BMP180 sensor.
Arduino UNO.
LCD Display.
Inverter.
Battery.
Charging Circuit.
Wind.
Voltage sensor.
Web Page.
SD card.

Software’s used:

Python Language.
Express SCH for Circuit design.

video:

HVS-4698. CANSAT – Weather monitoring satellite from ground station

hvsadmin — Wed, 06 May 2026 11:31:41 +0000

A CanSat is a miniaturized satellite system designed to simulate the functionalities of a real satellite within the volume of a soda can. This project presents the development of a low-cost, compact CanSat system for real-time weather monitoring and data transmission to a ground station. The system is built around the Teensy 4.0 microcontroller, which serves as the central processing unit for collecting and managing sensor data.

The CanSat integrates multiple sensors, including an air quality sensor, BMP180 barometric pressure sensor for temperature and pressure measurement, a gyroscope for orientation detection, and the INA219 current sensor for power monitoring. A GPS module provides real-time location tracking, while an MMC card is used for onboard data logging. Wireless communication between the CanSat and the ground station is achieved using XBee modules, enabling reliable telemetry transmission.

Power is supplied by a battery system regulated through the LM2596 voltage regulator to ensure stable operation. The system also includes actuators such as a servo and DC motors controlled via a driver circuit, along with a buzzer for status indication.

During operation, the CanSat collects atmospheric parameters such as temperature, pressure, altitude, and air quality, processes the data onboard, and transmits it to a PC-based ground station for monitoring and analysis. The proposed system demonstrates an efficient, scalable, and cost-effective approach to environmental data acquisition, making it suitable for educational purposes and preliminary aerospace research.

Objectives:

To design and build a compact CanSat system using a Teensy 4.0 microcontroller.
To measure environmental parameters such as temperature, pressure, altitude, and air quality using sensors like BMP180 barometric pressure sensor.
To monitor system power using the INA219 current sensor.
To obtain real-time location data using a GPS module.
To transmit collected data wirelessly to a ground station using XBee modules.
To store sensor data locally on an MMC/SD card for backup.
To provide alerts using a buzzer and control mechanisms like servo and DC motors.
To develop a low-cost and efficient weather monitoring system for educational and research purposes.

Components:

Battery power.
LM2596.
Teensy 4.0 microcontroller.
Air quality sensor.
BMP180.
GYROSCOPE
IA219 module.
GPS
DC Motor.
Buzzer.
Servo motor.
XBEE Transmitter and Receiver.

Software’s used:

Arduino IDE for compiling and dumping code into Microcontroller
Express SCH for Circuit design.

video:

HVS-2791.Energy Efficient Realtime Outdoor Air Quality Monitoring System Using Waspmote Pro Board, SGP30

hvsadmin — Mon, 19 Jan 2026 10:33:18 +0000

Rapid urbanization and industrialization have significantly increased outdoor air pollution, posing serious risks to human health and the environment. To address this challenge, this project presents an Energy Efficient Real-Time Outdoor Air Quality Monitoring System using the Waspmote Pro Board, SGP30 multi-gas sensor, BMP180 sensor, and a Wi-Fi PRO module. The system continuously monitors key air quality parameters such as CO₂ equivalent (CO₂eq), Total Volatile Organic Compounds (TVOC), and ambient temperature in real time.

The SGP30 sensor is used to measure CO₂eq and TVOC levels, while the BMP180 sensor measures ambient temperature. Based on predefined threshold values, the system provides immediate visual and audible alerts. A green LED indicates normal CO₂ levels, a yellow LED turns ON when CO₂ exceeds 500 ppm, and a red LED along with a buzzer is activated when CO₂ exceeds 600 ppm, indicating hazardous air quality. Additionally, the buzzer is triggered when the temperature rises above 35 °C to warn of high-temperature conditions.

All measured parameters are displayed locally on an LCD display for real-time observation. Using the Wi-Fi PRO module, sensor data including temperature, CO₂eq, and TVOC are uploaded to the ThingSpeak cloud platform, enabling remote monitoring, data visualization, and long-term analysis. The proposed system is low power, reliable, and suitable for continuous outdoor deployment, making it an effective solution for smart city applications, environmental monitoring, and public safety enhancement.

Objectives of proposed system are:

 To monitor CO₂ equivalent (CO₂eq) and TVOC levels using the SGP30 sensor.

 To measure ambient temperature using the BMP180 sensor.

 To implement a Wi-Fi PRO module for wireless data transmission.

 To indicate air quality status using Green, Yellow, and Red LEDs.

 To activate a buzzer when CO₂ exceeds 600 ppm or temperature exceeds 35 °C.

 To display real-time values on an LCD display.

 To upload sensor data to the ThingSpeak cloud for remote monitoring and analysis.

The major building blocks of this project are:

Adapter power supply.
Waspmote Pro Board
BMP180
SGP30(CO2 AND TVOC) sensor.
Buzzer
LCD display.
YELLOW, GREEN LEDs.
WI-FI PRO MODULE.

Software’s used:

Embedded C programming.
Waspmote IDE for dumping code.
Express SCH for Circuit design.

Regulated power supply:

Block Diagram:

video: