Meeting with Your TA

Description

By the Thursday of the third week, you must have a project approved, and should be ready to get working! At this time, you'll need to log into PACE and submit your schedule for the semester. Please be sure to make this as accurate as possible because once it's submitted, it can only be changed manually. Making a block of your schedule red means that you are unavailable during that time.

Once each person on your team has submitted their schedule, your TA will be able to easily check for available times to schedule a weekly meeting. Your TA should contact you, usually by the fourth week, via email, to set up a weekly meeting schedule at mutual convenience. During the first weekly meeting, your TA will assign your team a locker and a lab kit.

Weekly meetings with your TA are required and will be held throughout the entire semester until demonstrations are completed. Your TA is your project manager. The "homework" of the course consists of preparing for the weekly meetings. Your TA will evaluate your lab notebook each week, provide feedback, and recommend improvements. At each meeting you will be expected to present your progress since your last meeting, plans for the coming week, and any technical or administrative questions you need to discuss with your TA. You are expected to arrive on time and prepared to make good use of your time with your TA. Your TA may require that each team member to fill out the Progress Report Template and submit it to them prior to each weekly meeting.

Requirements and Grading

Attendance and participation in weekly meetings is required and will affect Teamwork and Lab Notebook scores. If you can't make it to a particular weekly meeting, it is your responsibility to inform your TA prior to the meeting time and set up an alternate time.

Submission and Deadlines

Your schedule must be submitted by the end of the third week of class and you will receive an email from your TA shortly after. Your first meeting with your TA should be during the fourth week of the semester.

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.