Final Report

Video Lecture

Video, Slides

Description:

The Final Report Guidelines are the primary reference document for this assignment.

Requirements and Grading:

The Final Report is held to professional standards of language and format and is evaluated by staff in the ECE Editorial Services, who also check theses and dissertations for the department. The report is also evaluated for technical content and organization by the course staff. , but here are some pointers:

  1. If you didn't click the link above, the Final Report Guidelines should be your first stop.
  2. Use a template to help get the formatting right (Microsoft Word template or LaTeX template).
  3. Since your Final Report is similar in purpose to a thesis, you may find the Thesis Writing Guidelines helpful for style and formatting.
  4. For citations, you may also find the IEEE Citation Reference guide useful.
  5. Please note the maximum number of pages (25) allowed for the final report. You will be penalized for going over the maximum number of pages and/or not following the prescribed format.

    6. Submission and Deadlines:

    The Final Report document should be uploaded to My Project on PACE in PDF format by the deadline on the Calendar.

ML-based Weather Forecast on Raspberry Pi

Xuanyu Chen, Zheyu Fu, Zhenting Qi, Chenzhi Yuan

Featured Project

#Team Members

Zheyu Fu (zheyufu2@illinois.edu 3190110355)

Xuanyu Chen (xuanyuc2@illinois.edu 3190112156)

Chenzhi Yuan (chenzhi2@illinois.edu 3190110852)

Zhenting Qi (qi11@illinois.edu 3190112155)

#Problem

Weather forecasting is crucial in our daily lives. It allows us to make proper plans and get prepared for extreme conditions in advance. However, meteorologists always get it wrong half of the time and still keep their job :) To overcome the limitations of traditional weather forecasting, machine learning models have become increasingly important in weather forecasting. Building our own weather forecast ML system is a perfect idea for us to analyze vast amounts of area data and generate more accurate and timely weather predictions on the go in our surrounding areas.

#Solution Overview

A weather forecast system can be created by using a few different hardware components and software tools. Our solution mainly consists of two parts. For weather measurement and data collection, temperature, humidity, and barometric pressure sensors are considered the main components. A machine learning-based algorithm is to be applied for data analysis and weather predictions.

#Solution Components

##Hardware Subsystem

Due to the complexity of weather conditions, our system incorporates the following weather indicators and their corresponding collectors:

-a barometric pressure sensor, a temperature sensor, and a humidity sensor

-a digital thermal probe for heat distribution

-an anemometer for wind speed, wind vane for wind direction, and rain gauge for precipitation

The aforementioned equipment would be integrated into a single device, and weatherproof enclosures are needed to protect it. Plus, a Raspberry Pi, either with built-in wireless connectivity or a WiFi dongle, is required for conducting computations.

##Software Subsystem

A practically usable weather forecast system is supposed to make reliable predictions for real-world multi-variable weather conditions. We apply Machine Learning techniques to suffice such generalization to unseen data. To this end, a high-quality dataset for training and evaluating the Machine Learning model is required, and a specially designed Machine Learning model would be developed on such a dataset. Once a well-trained system is obtained, we deploy the such model on portable devices with easy-to-use APIs.

#Criterion for Success

1. The weather measurement prototype with sensors should be able to accurately collect the temperature, humidity, and barometric pressure. etc.

2. A machine learning algorithm should be successfully trained to make predictions on the weather conditions: rainy, sunny, thunderstorm, etc.

3. Our system can forecast the weather in Haining, in real-time, and/or longer-period forecast.

4. The forecasted weather information could be demonstrated elegantly through some UI interface. A display screen would be a baseline, and an application on phones would be extra credit if time permitted.

5. Extra: Make our own weather dataset for Haining. If good, make it open-source.

#Work Distribution

**EE Student Zheyu Fu**:

-Design the sensor module circuit

-Development of visualization interface

**ECE Students Xuanyu Chen & Zhenting Qi**:

-Weather data collection and analysis

-Build and test Machine Learning model on Raspberry Pi

**ME Student Chenzhi Yuan**:

-Physical structure hardware design

-Proper distribution of the sensors to collect accurate data on temperature, humidity, barometric pressure, etc.