VIT Hackathon Starter Kit

Industrial activities have been one of the primary source of Global Warming as the unregulated energy consumption in industries lead to emission of greenhouse gases in atmosphere. Though the problem may appear easy to solve, identifying right time, volume and area of savings have always been challenging in saving energy. This kit provides the best libraries to build your solution for improving the energy efficiency. The libraries are listed in different sections based on the functionality they provide e.g. - OLAP data visualization - Exploratory Data Analysis - Data Labeling - AutoML Depending on your solution needs, you can pick the needed libraries from the below options and build your solution with them. This kit also acts as a template for submitting your hackathon solution in the form of a kandi kit. You should create relevant solution sections in your kit and add the libraries used under those sections. You must add the two important sections Kit Solution Source and Kit Deployment Instructions. Please see the sections below to understand this better. Problem statement: Jumpstart and create an application which can be used by energy management professionals using the below data set. https://github.com/jojo62000/Smarter_Decisions/raw/master/Chapter%206/Data/Final_SolarData.csv https://github.com/jojo62000/Smarter_Decisions/blob/master/Chapter%203/Data/BO5341_IoTData.csv Expected outcome in the application 1. OLAP operation of the data in front end (dice, slice, roll up/ down, filter) 2. Ability to notify significant changes in the time series dataset imported in the tool 3. Ability to select from and to time stamp in the time series visualization and give a label or annotation 4. Annotation tool - Data labelling where the customer can import the data and the multiple columns render it in the chart where they can select from all and to frame and labelled the part and save in the database. 5. Exploratory data analysis- Where they can explore the data and find its relationship with the different parameters. 6. Prediction Analysis/ modelling - Where they can pass the data and application has to automatically select which model is best and show it’s all the model accuracy results. 7. Have a dashboard to display aggregated values. 8. Results should be in pictorial representation. 9. Data cleaning/Data sanitisation must be done (Should not have null values). 10. Working video of the application is expected.

Dataset

Sample dataset reference. https://github.com/jojo62000/Smarter_Decisions/raw/master/Chapter%206/Data/Final_SolarData.csv https://github.com/jojo62000/Smarter_Decisions/blob/master/Chapter%203/Data/BO5341_IoTData.csv

OLAP data visualization

OLAP data visualization libraries can be used to drill down, roll up or slice and dice the data from OLAP databases.

Exploratory Data Analysis

The data exploration helps in doing extensive analysis of different data types and in assisting to understand the patterns. Data visualisation helps in representing the insights graphically. Libraries in this section are used for analysis and visualisation of data.

Data Labeling

Libraries in this section are used for annotating data for creating training data for machine learning.

Auto ML

Listed libraries can be used for auto machine learning. AutoML will pick the best machine learning algorithm based on the dataset.

Sustainability Hackathon RapidHack initiative in partnership with VIT Chennai and IAEMP

Special Thanks to our organizers, Dr.S.Angalaeswari & Dr.K.Jamuna, VIT-IAEMP Coordinators, Vellore Institute of Technology, Chennai. Dr.B.Somasundaram, Vice-President, IAEMP Ms.Sahaana .V, Executive Council Member,IAEMP Coimbatore Chapter. Mr.Gokul Ganesan, Executive Council Member, IAEMP Coimbatore Chapter.