💻 Software & Environment Setup
Data Science & Analytics
The computational toolchain and environment used throughout AI For Energy Load Forecasting In Smart Grids.
Demand training for researchersEnergy load forecasting for PhD studentsEnergy load forecasting training Saudi ArabiaSmart workshopGrid workshopEnergy load forecasting training Botswana
# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install numpy pandas scikit-learn matplotlib seabornPython 3.11 + Jupyter
Interactive analysis notebooks
Interactive analysis notebooks
pandas / NumPy
Data wrangling & numerics
Data wrangling & numerics
scikit-learn
Modelling & evaluation
Modelling & evaluation
Plotly / Seaborn
Statistical visualisation
Statistical visualisation
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in AI For Energy Load Forecasting In Smart Grids
e-Certificate and e-Marksheet issued on successful completion.
