time series analysis, machine learning, and statistical modeling
Energy consumption forecasting is crucial for efficient power grid management, demand planning, and renewable energy integration. This project involves analyzing historical energy usage data from smart meters, industrial grids, and household appliances. The goal is to develop predictive models that help energy providers optimize load distribution, reduce costs, and prevent power outages. The project includes data preprocessing, feature engineering, model selection, evaluation, and deployment.
Programming Languages: Python, R Libraries & Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Statsmodels, Facebook Prophet, XGBoost Databases: PostgreSQL, MySQL, MongoDB (for historical data storage) Tools & Platforms: Jupyter Notebook, Google Colab, AWS/Azure (for cloud-based analytics), Tableau/Power BI (for visualization) Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://archive.ics.uci.edu/
https://www.iea.org/
https://datasetsearch.research.google.com/
