The CleanEnergyBot is a Telegram bot providing real-time electricity usage, CO2 forecasts, and energy-saving tips in Ireland, using data from EirGrid and GPT-3 analysis. It helps users make eco-friendly energy choices by comparing emissions data with EU standards.

We propose Compressive Sensing and Deep Learning framework (CS-DL) for multiple satellite sensor based data fusion. It’s aims to improve spatial and temporal resolution for long term analysis. Compressive Sensing is used as an initial guess to combine data from multiple sources. Deep Learning model, using Long Short Term Memory Neural Network (LSTM/RNN) refines and further improves the resulting data fusion output from CS. Our CS-DL framework has been tested to fuse CO2 from the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). It achieves lower errors and high correlation compared with the original data. This work demonstrates the use of CS-DL for fusing CO2 from NASA Orbiting Carbon Observatory-3 and GOSAT2 at higher resolution.

We have a dataset which contains various features of the car based on which we predict the Carbon dioxide emission.

Performs an SDA analysis in an EE-MRIO framework using WIOD world input output tables and CO2 emissions data (2000-2014). Includes code on extraction of data.

In this project, data mining and time series analysis algorithms are used to predict whether people are present in a room based on physical information such as CO2 or humidity levels in the air.

Data scraped from the Australian energy market to focus on the analysis of the dataset time features behaviour. The evaluation is complemented by an initial attempt of a forecast of CO2 emissions from electricity production.

The project look's at the CO2 emmisions of the aviation industry. This project was conducted in the context of the Applied Data Analysis course at EPFL.

Project using linear regression and data science to predict and analysis of global warming using co2 emission over years

A Python web scraper using Selenium to extract global energy data from the IEA website. Collects data such as energy mix, electricity mix, CO2 emissions, and yearly statistics for countries. Saves results in a CSV file for analysis. Ideal for researchers and data enthusiasts interested in energy trends.

ASSIGNMENT FOR BATCH 2 Apply where it is necessary i:e data preprocessing,linear regression ,polynomial regression, and decision tree etc on the following data-set and compare the accuracy of all regression and take screenshots of output prediction and accuracy of all the regression which you are going to apply and upload all the codes on github with screenshot.Dont try to copy others because we give you just half data-set and when you show us output for future analysis we will compare your values with the remaining half of the data-set and then give you marks. Explanation of the data-set is as follows: 1. Take 50 startups of two countries and find out which country is going to provide best profit in future. 2. Annual temperature between two industries is given. Predict the temperature in 2016 and 2017 using the past data of both country. 3. Data of global production of CO2 of a place is given between 1970s to 2010. Predict the CO2 production for the years 2011, 2012 and 2013 using the old data set. 4. Housing price according to the ID is assigned to every-house. Perform future analysis where when ID is inserted the housing price is displayed. 5. Data of monthly experience and income distribution of different employs is given. Perform regression.

Analyzing CO2 emissions trends using Python, exploring population and GDP impacts. Implemented ARIMA, LSTM, RandomForestRegressor, and Prophet models for forecasting.

Matlab codes and data in .mat format for figure generation of CO2 storage logistic curve analysis

A repository containing the code and data used for my Bachelor Project "CO2 and The Internet, a quantitative study on the environmental impact of the top Web sites in the world". It is a comprehensive analysis of the internet's emissions and its effects on the environment.

Comprehensive analysis and forecasting of CO2 emissions across multiple countries. Utilizes machine learning and time-series models to predict future emissions based on historical data from 1990-2020.

The project involves analyzing a dataset of vehicles to gain insights into their CO2 emissions. The dataset was sourced from an open data repository and the analysis was conducted using Python programming language.

Predictive analysis of vehicle CO2 emissions using RandomForest with a deep dive into feature influence via SHAP values. This project leverages machine learning to understand and predict emissions based on extensive vehicle data.

Carbon Dioxide is a major component of Green House Gas (GHG) emissions, accounting for 81% of the total emissions. Released naturally (respiration, ocean drive, and decomposition) or as a consequence of human activity (cement production, burning of fossil fuels such as coal, oil and natural gas, etc.), carbon dioxide has played a significant role in Global Warming. It is a phenomenon whose existence can be found in the melting of polar ice caps or progressive rise in the annual global temperatures. India, an emerging economy is the fourth largest producer of CO2 emissions, behind China, USA and the European Union. A nation, with the drive to become a leading superpower in the coming years, India’s booming economy and development poses a serious challenge to the levels of carbon dioxide emissions. Overtaking Russia, to become the third largest producer of electricity, India still relies on coal as the biggest source of electricity, whose burning yields CO2 into the atmosphere. Awaken by the adversities of CO2 emissions, India has signed the Paris Agreement and pledged to reduce the CO2 levels to 30-35% of the level in the year 2005. This agreement is set to be starting in 2020. To have a better understanding of what the challenge is going to be in the year 2020, prior to the implementation of the Paris agreement, this paper aims at forecasting the levels of CO2 emissions and its constituents (solid, liquid and gaseous fuels) in India. The technique used in the forecasting is Exponential Smoothing. Starting from 1960, first two years data has been used for initialization and the value of model parameters (alpha and beta) has been optimized with data from 1962-2017 using Solver. The forecast is estimated for the years 2018-2020. Keywords: Predictive Analytics, CO2 emissions, Time Series Analysis, R, Double Exponential Smoothing, Paris Agreement

Indoor and Greenhouse Control via Arduino and Beagle Bone Black A environment control mainly for indoor and greenhouse growing featuring: watering / hydroponic water cycles timing grow-light timing air flow control based on humidity and temperature water tank monitoring (temperature, EC [electric conductivity] water level) Data-Logging Smart UI based on local Web-Server Future Version will include: more Sensor Data like ph and co2 levels automatically adding fertilizers and RO-water (Reverse Osmosis) to the water reservoir camera analysis, analyzing plants via pictures from an integrated camera

A short R script that attempts to visualize carbon emissions by country in three different ways and the same visualizations after they were exported to Adobe Illustrator so additional features could be added to the visualizations. This data set came from the Carbon Dioxide Information Analysis Center, http://cdiac.ornl.gov/, via Gapminder’s site.(http://www.gapminder.org/data) This data shows the CO2 emissions per capita of 201 countries in metric tons. I chose to look at the top 50 emitters in 5 year increments between 1970 and 2010, which seemed long enough to see significant trends, but not so far back as to be overwhelming or suffer from large amounts of missing data. To determine who the top 50 emitters were I summed each row (from 1970 – 2010), sorted from largest to smallest and took the countries with the 50 largest amounts. The categorical variables being compared are Country and Year.

Big Data Analysis and Green Computing: Profiling HPC Power and Tracking CO2 Emissions

Analysis of factors that contribute to global and national CO2 emissions, using data from the World Bank’s World Development Indicators.

CO2 and Climate Change Data Analysis

This is a data analysis of co2 emissions and other environmental factors from XX century until 2020 by countries

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Exploring CO2 emissions related dataset in Tableau

A data analysis(descriptive, diagnostic and predictive) of Carbon Dioxide emission Vs GDP per capita over the selected countries.

Python application to perform Data Analysis on CO2 Emission for different countries.

This is a Guru99 live python project on CO2 Emission for different countries

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Data Visualization Final Project. CO2 Emissions Analysis.