Konstantinos Christakos’ Post

🌟 Excited to Share My Latest Project! 🌟 I’ve just completed an intermediate-level project on Renewable Energy Forecasting using pandas and Machine Learning. This project was a fantastic opportunity to dive deep into data analysis and predictive modeling. Here’s a quick overview: Project Duration: 1 Week Key Components: Data Loading & Understanding Data Cleaning & Feature Engineering Exploratory Data Analysis (EDA) Modeling & Hyperparameter Tuning Model Selection & Inference This project not only enhanced my technical skills but also provided valuable insights into the renewable energy sector. 🌍🔋 A big shoutout to the project contributor, Ibrahim, for the comprehensive guidance and resources. 🙌 #RenewableEnergy #MachineLearning #DataScience #Python #pandas #EDA #FeatureEngineering #Modeling #Sustainability AMDARI

I’m happy to share this Greenhouse Gas Analysis Project (using #Python and #Pandas) which I was working on. In this project I took two datasets about the CO2 annual emissions by countries and the Greenhouse gas emitted by different type of sources, making a data exploration, cleaning and analysis of tops, correlations and variation of the data, the following was obtained: 1. The country with the most CO2 emissions is the United States, followed by China. 2. Asia is the continent with the most annual CO2 emissions. 3. South America only represents 7.8% of the CO2 emissions of the entire continent of America. 4. The variables "Electricity and Heat" and "Industry" has a strong correlation. If you want to see the Python code with more insights you can check it out here: https://lnkd.in/ecgbqEWr #DataAnalysis #DataCleaning #ExploratoryAnalysis #GreenhouseGas #CO2

🌍 New Project on Energy Power Plants in Germany 🔋 I’ve just completed an in-depth analysis of power plants across Germany . This project dives into the distribution, capacity, and types of energy sources used in these regions. By leveraging Python for data analysis and visualization, I explored how these energy sources shape the current energy landscape, alongside the historical developments that have influenced it. This analysis not only helped me apply the skills I've been developing in Python but also gave me a deeper understanding of the evolving world of energy. Feel free to check it out and share any feedback—it would definitely help me on my learning journey! 💡 🔗 https://lnkd.in/e6H_nK65 #Energy #DataAnalysis #RenewableEnergy #PowerPlants #Sustainability #Germany #Python #Analysis #DataViz

Finished the Global Renewable Energy Analysis as the #Day88 of #100daysofcoding challenge for Data Science and Data Analytics. For more, Have a look into my IPYNB. #dataanalytics #dataanalysis #datascience #datavisualization #datamodeling #analysis #EDA #energy #python #numpy #pandas

🚀 Wind Turbine Damage Detection Model using PyTorch🚀 I'm excited to share the first stage of my Wind Turbine Damage Detection project! 🎉 This model aims to identify various types of damage on wind turbine blades, such as cracks, erosion, lightning damage, and panel issues. While it's not a perfect model yet, it's a strong start, and I'll be gathering more data to improve its accuracy over time. This project is not just about creating a damage detection model but also about demonstrating how you can build your own model using a custom dataset and developing a custom data loader. I've documented the process and included the code on my GitHub repository. Repo: https://lnkd.in/dtZYWFmV 📂 Project Highlights: *Custom dataset and data loader Image annotations in COCO format *Training script for damage detection model *Organized folder structure for ease of use *Check out the repository here: Wind Turbine Damage Detection Model Feel free to explore, give feedback, and contribute! Together, we can make this model more robust and accurate. 🌟 #MachineLearning #DeepLearning #ComputerVision #DamageDetection #WindTurbine #DataScience #OpenSource #Python

Over the past three months, I've been dedicated to investigating user-friendly and accessible animated map solutions. I've found a solution that meets these criteria perfectly! 🤗... Python, Plotly, and the Mapbox API for geographic data integration, the Free public Token. This visualization provides insights into how solar irradiance levels change over the course of the years across different departments: -  It shows the average amount of solar energy received per unit area over a year in each departament within the Colombian Caribbean region. - It allows for comparisons between different departments within the Colombian Caribbean region. Variations in the yearly mean irradiance levels across regions can highlight geographical differences in solar energy potential due to factors like latitude, altitude, and local climate conditions. - This type of visualization can help stakeholders identify regions with high solar energy potential for potential development or investment opportunities. Basemap: geojson from Colombia layer: Irradiance data since 2012, sourced from IDEAM code: https://lnkd.in/eAswdD59 #personalgrowth #solarenergy

🌿 We’re thrilled to announce our new partnership with Cecil! Cecil is a nature data platform built for data and science teams, making data consistent, joinable, and analysis-ready. By teaming up with Cecil, Chloris Geospatial’s aboveground biomass stock and change data is now more accessible than ever, supporting data teams in carbon markets and corporate supply chain emissions reporting. With Cecil's Python Software Development Kit (SDK), accessing Chloris data is effortless: ◾ Request datasets for any area of interest, with no minimum area requirements ◾ Configure data to your preferred Coordinate Reference System (CRS) and resolution ◾ Receive data directly in a database, optimized for your analysis Discover more about our partnership and how it streamlines access to reliable carbon data. Read Cecil’s post - link in comments! #data #geospatial #carbonaccounting

🔆 Excited to Share My Latest Project on Solar Radiation Prediction! 🌞 I am thrilled to showcase my recent work on predicting solar radiation using data science and machine learning techniques. This project leverages various tools and libraries such as pandas, numpy, matplotlib, seaborn, sklearn, xgboost, and optuna. #KeyHighlights: - Dataset: A comprehensive dataset containing 32,686 entries with features such as UNIXTime, Data, Time, Radiation, Temperature, Pressure, Humidity, Wind Direction, Speed, TimeSunRise, and TimeSunSet. - Preprocessing: Data cleaning and preprocessing steps including handling missing values, data normalization, and feature engineering. - Modeling: Implemented XGBoost Regressor for predictive modeling, optimizing hyperparameters using Optuna to enhance model performance. - Evaluation: Achieved a high R-squared score, showcasing the model's accuracy in predicting solar radiation. #ToolsandTechnologies: - Python Libraries: pandas, numpy, matplotlib, seaborn, sklearn, xgboost, optuna - Data Visualization: Used seaborn and matplotlib for insightful visualizations - Model Evaluation: Evaluated the model performance using metrics like r2_score I am proud of the results and the insights gained from this project. Solar radiation prediction is crucial for optimizing solar energy generation and improving the efficiency of solar panels. #Lets_Connect! Feel free to reach out if you are interested in discussing this project further or exploring collaboration opportunities in the field of data science and renewable energy. #DataScience #MachineLearning #SolarEnergy #Python #XGBoost #Optuna #RenewableEnergy #DataVisualization

🚀 Just completed the "Time Series Analysis and Forecasting with Python" certification, which provides a solid foundation in advanced forecasting techniques. The course covered essential methods for analyzing complex time series data, especially applicable in dynamic sectors like #renewable_energy. 🌐 This expertise in time series forecasting is invaluable for optimizing wind energy production, integrating #hybrid_systems, and improving the performance of Battery Energy Storage Systems (#BESS). In addition to the course material, knowledge of deep learning and transformers further enhances the capability to develop predictive models that anticipate energy demand and production with greater precision. 🔍 By combining these cutting-edge techniques with traditional time series analysis, it's possible to unlock powerful insights into energy forecasting, driving innovation in sustainable energy management. #DataScience #TimeSeriesAnalysis #DeepLearning #Transformers #RenewableEnergy #WindEnergy #BESS #Python #AI #Forecasting

🚀 Excited to share a project: Solar Irradiance Tracking using Time Series Prediction! 🌞🔍 🌐 Project Overview: In this project, I developed a system that predicts solar irradiance for the next hour using advanced time series forecasting methods. Accurate solar irradiance predictions are crucial for optimizing the performance and efficiency of solar energy systems. 🔧 Technologies Used: - Python/Jupyter Notebook for data analysis and modeling - Node-RED for creating a user-friendly interface 📊 Key Steps in the Project: 1. Data Preprocessing: Cleaned and prepared the solar irradiance data, handling missing values and transforming the dataset for better analysis. 2. Exploratory Data Analysis: Visualized the time series data to understand patterns and trends, including creating interactive charts with Plotly. 3. Feature Engineering: Identified and incorporated lagged values as features for our models. 4. Model Development: Built and evaluated several models including LSTM, Bidirectional LSTM, KNN, SVR, and ARIMA to predict solar irradiance. 5. Model Comparison: Compared the performance of different models using metrics like RMSE and MAE to determine the best model. 📈 Results: Achieved impressive results with the models, particularly with the Bidirectional LSTM and SVR models, which provided highly accurate predictions. Here are some of the performance metrics: - LSTM R²: 98.57 - Bi-LSTM R²: 98.87 - KNN R²: 98.67 - SVR R²: 98.86 - ARIMA R²: 98.15 This project not only highlights the power of machine learning in the field of renewable energy but also demonstrates the potential of data-driven approaches to solving real-world problems. 💡 Future Work: I plan to further enhance the system by integrating real-time data and deploying the solution for continuous monitoring and prediction. Feel free to check out the https://lnkd.in/dvdsavyw for more details and the complete code. Your feedback and suggestions are welcome! #MachineLearning #TimeSeries #RenewableEnergy #SolarPower #DataScience #AI #Python #JupyterNotebook #NodeRED #Innovation #Sustainability