Project

# Title Team Members TA Documents Sponsor
28 Electric Load Forecasting (ELF) System
 Ao Zhao
Liyang Qian
Yihong Jin
Ziwen Wang
 Xiaoyue Li design_document3.pdf
final_paper2.pdf
proposal1.pdf
 Ruisheng Diao
# Electric Load Forecasting (ELF) System

# Team members:

Ao Zhao, aozhao2

Ziwen Wang, ziwenw5

Liyang Qian, liyangq2

Yihong Jin, yihongj3

# Problem
Electric load forecasting (ELF) is a method that takes into account unstable factors, such as weather conditions and electricity prices, to predict the demand for electricity. Many utility companies rely on manual forecasting techniques based on specific datasets, but these methods may lack accuracy when fine-grained time particle forecasting is required. To accurately predict expenses on electricity and construct reliable infrastructures that can withstand a certain electrical load, utility companies need more advanced and reliable forecasting methods.

# Solution Overview
The electric load forecasting system is a powerful tool for predicting future electric load usage based on dedicated hardware and AWS services. By combining the data collection subsystem, data storage subsystem, prediction subsystem, query API subsystem, and web page subsystem, customers can easily retrieve and visualize the predicted electric load usage and use it for planning and optimization purposes. The system is designed to be accurate, effective, reliable, and easy to use, providing customers with a complete solution for electric load forecasting.

# Solution Components
[Data Collection Subsystem] - This subsystem is responsible for collecting real-time data on electric load usage. The data collection hardware is designed to be reliable, scalable, and capable of handling large volumes of data. The collected data is then sent to the data storage subsystem for further processing through AWS IoT Core.
Hardware I. Smart meters to collect voltage, current, power and other data which further improve the ability to collect information
Hardware Ⅱ. A transmission communication device that connects a smart meter to software or concentrator
Hardware Ⅲ. Sensors that collect some relevant external factor data data (ex. Temperature sensor)

[Data Storage Subsystem] - This subsystem is responsible for storing the collected data in a secure, scalable, and durable storage system. The data is stored in a format that is compatible with the Forecast DeepAR+ algorithm. AWS S3 provides a highly available and cost-effective storage solution that is suitable for storing large volumes of data.

[Prediction Subsystem] - This subsystem is responsible for generating accurate predictions of future electric load usage based on the collected data. The Forecast DeepAR+ algorithm is a state-of-the-art machine learning algorithm that is designed for time-series forecasting. The AWS Forecast service makes it easy to generate accurate predictions at scale. The output of this subsystem is a forecast of future electric load usage that can be used for planning and optimization purposes.

[Query API Subsystem] - This subsystem provides a RESTful API that allows customers to retrieve the predicted electric load usage for a specified time period. The API is designed to be secure, scalable, and easy to use. Customers can send requests to the API with the necessary parameters, and the API will return the predicted electric load usage in a format that is easy to understand and use.

[Web Page Subsystem] (Optional) - This subsystem provides a user-friendly web interface for accessing the predicted electric load usage. The web page is built on top of the query API and allows customers to easily select the time period they are interested in and view the predicted electric load usage in a graphical format. The web page is designed to be responsive, easy to use, and accessible from any device with a web browser.

# Criterion for Success
Accuracy: The system should generate accurate predictions of future electric load usage. The accuracy of the predictions should be high enough to enable effective planning and optimization of electric power usage.

Scalability: The system should be capable of handling large volumes of data and generating predictions for a large number of electric load customers. The system should be able to scale up or down as the demand for electric power changes.

Reliability: The system should be designed to be highly reliable and available. It should be able to handle failures gracefully and recover quickly from any disruptions in service.

Security: The system should be designed to be secure and protect customer data from unauthorized access or disclosure. The system should use industry-standard encryption and access controls to protect customer data.

Ease of Use: The system should be designed to be easy to use and accessible to a wide range of customers. The query API should be easy to understand and use, and the web page interface should be intuitive and user-friendly.

Cost-Effectiveness: The system should be designed to be cost-effective and provide good value for money. The cost of running the system should be reasonable and should not be a significant barrier to adoption.

# Distribution of Work

Yihong Jin, Computer Engineering:

As a [AWS Certified Solutions Architect - Professional](https://www.credly.com/badges/1e4aa7a1-3ee6-4dd8-94c5-c015a85c3b84/linked_in_profile), design and implement the software architecture of this solution based on AWS services. Responsible for building the data pipeline which ingest raw data from by dedicated hardwares and prepare it for Machine Learning model training.

Liyang Qian, Computer Engineering:

Train the deepAR+ model with data stored in AWS S3 and build the API to enable customers to take advantage of forecasting results.

Ao Zhao, Ziwen Wang, Electrical Engineering :

Design the hardware used to collect the data and connect smart meters and software through transmission devices or specific communication methods to realize data interaction between each other.

Electromagnetic Launch System with Switchblade Drone

Zheng Fang, Shuyang Qian, Xinyu Xia, Ruike Yan

Featured Project

# TEAM MEMBERS:

Shuyang Qian (sq8)

Zheng Fang (zhengf4)

Xinyu Xia (xinyux4)

Ruike Yan (ruikey2)

#TITLE OF THE PROJECT:

Electromagnetic Launch System with Switchblade Drone

# PROBLEM:

The Switchblade UAVs in use today tend to use pneumatics for power. It has been limited by its launching speed, cost, and portability. Making use of electromagnetic technology can improve the design. The project aims to develop an electromagnetic launch system which can launch switchblade drone well.

# SOLUTION OVERVIEW:

The project involves the development of an electromagnetic launch system and a switchable drone. The launch system is designed to propel a fixed-wing drone to a relatively high speed, using electromagnetic forces. The drone is equipped with a switchable wing mechanism that allows it to be housed within the launching track during launch and then deployed for flight after exiting the launching system. There are several main steps to finish the project well:

Design and construction of the launch system

Development of the foldable wing mechanism

Integration of subsystems

Testing and validation Overall, the project's success will depend on the effective implementation of these solutions, which will require careful planning, design, and testing to achieve the desired outcome of a functioning electromagnetic launch tube with a switchblade drone.

# SOLUTION COMPONENTS:

The solution will consist of the following components:

Electromagnetic launch system: the system includes multiple sets of acceleration coils, a base to hold the coils, a base with both a guide slot for the horizontal movement of the ejection ram, and a launch cart to hold the drone.

Switchblade drone: the system includes the main body of the drone, a pair of foldable wings, a folding device powered by a torsion spring, and an attachment device for the drone to the ejection ram.

Electrical control system: the system mainly controls the charging and discharging of the coil, the main components are Hall Effect Sensors, N-Channel Power MOSFETs, MOSFET Heatsinks, High Speed Power MOSFET Drivers, Resistors, Momentary Switch.

# CRITERION OF SUCCESS:

The success of the project will be determined by the following criteria:

Portability: Weather the system is small and portable enough to be carried in a suitcase or other boxes.

Speed of the launched plane: The speed of the plane needs to be fast enough so that it can travel enough distance and realize some additional functions.

Safety: The system should not cause danger to the operator or other people around it. Potential dangers are, for example, Mechanical scratches and electric leakage.

Stability: The success rate of launching the plane, and the route of the plane after each launching should be similar.

# DISTRIBUTION OF WORK:

Shuyang Qian (ME): Responsible for designing and constructing the mechanical part of electromagnetic launch system including the guide rails, fixing parts and installation of coils.

Zheng Fang (ECE): Responsible for designing and soldering the circuit for controlling the charging and discharging of the coil.

Xinyu Xia (ME): Responsible for designing and constructing the switchblade drone which can be accelerated by the electromagnetic launch system and whose foldable wings can run well.

Ruike Yan (EE): Responsible for designing the control system for switchblade drone which lets the drone continues to fly after leaving the electromagnetic launch system.