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ArshidAli84/README.md

Interested to Work in the Field of Machine Learning, Power Systems and Research


  • 🔭 I’m currently working on 30-Ready-ML-Projects, a comprehensive repository showcasing practical machine learning applications across diverse domains, including smart grid and energy management.

  • 👯 My main research focus is on ML|DL|RL projects related to power and energy systems by learning Smart circuit breakers to avoid power system blackouts during natural disasters.

  • 🌱 I’m diving deep into Reinforcement Learning, exploring its applications in optimizing energy distribution within smart grids, ensuring efficiency and sustainability.

  • 👨‍💻 My projects span various applications in Machine Learning, Smart Grids, and Energy Management. Find them at My GitHub Profile.

  • 💬 Curious about Machine Learning, Smart Grids, and Energy Management? I'm passionate about discussing innovations and challenges in these fields.

  • 📫 Reach out to me at arshidali.yaho@gmail.com for collaborations, discussions, or inquiries.

Key Areas of Expertise:

Languages and Tools:

Arduino MATLAB MySQL Pandas Photoshop Python PyTorch Scikit-learn Seaborn TensorFlow

Top Languages

GitHub Stats

GitHub Streak

  • I leverage a comprehensive set of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, NumPy, Pandas, Matplotlib, SciPy, and PyPI to build and deploy predictive models. These models are specifically designed for optimizing smart grid functionalities, energy forecasting, and anomaly detection within power systems.

Relevant Projects and Contributions:

1. Smart Grid Optimization using Reinforcement Learning:

  • Implementation of a Reinforcement Learning model for optimal energy distribution within smart grid networks, resulting in significant enhancements in energy efficiency and cost reduction.

2. Energy Forecasting with Machine Learning:

  • Developed predictive models employing time series analysis and machine learning algorithms to forecast energy consumption patterns, facilitating efficient resource allocation in energy management systems.

3. Anomaly Detection in Power Systems:

  • Created an anomaly detection system utilizing machine learning techniques to identify and mitigate faults or anomalies in power systems, ensuring grid stability and resilience.

Publications and Research Contributions:

  • Co-authored multiple papers published in leading conferences and journals, focusing on the convergence of AI, smart grid technologies, and energy management.

Let's connect and explore the fascinating realm of AI-driven solutions in smart grids and energy management!

Popular repositories Loading

  1. Towards-Electric-Grid-Stability-using-ML-for-NTL-Detection Towards-Electric-Grid-Stability-using-ML-for-NTL-Detection Public

    SGCC equipment inspection using Histogram Gradient Boosting for health assessment.

    Jupyter Notebook 1

  2. ArshidAli84 ArshidAli84 Public

    Config files for my GitHub profile.

  3. Wine-Quality-Prediction-using-Machine-Learning Wine-Quality-Prediction-using-Machine-Learning Public

    This dataset enables wine quality prediction and binary classification. It comprises attributes related to red and white "Vinho Verde" wines in Portugal. Features include acidity, residual sugar, d…

    Jupyter Notebook

  4. Heart-Disease-Prediction-using-Machine-Learning Heart-Disease-Prediction-using-Machine-Learning Public

    Heart Disease dataset, donated in 1988, contains 303 instances with 13 features for classifying heart disease presence (0-4). It's a multivariate dataset from the field of life science, with attrib…

    Jupyter Notebook

  5. ArshidAli84.md ArshidAli84.md Public

    ReadmeFile

  6. KMeans-Clustering-for-Online-Facebook-Sale KMeans-Clustering-for-Online-Facebook-Sale Public

    Unveiling_the_Power_of_K_Means_Clustering_in_Python_Revolutionizing_Facebook_Live_Selling_in_Thailand_&_ML!

    Jupyter Notebook