Five key data applications within the electricity sector were identified as priorities for the Cal-Adapt: Analytics Engine. These were identified through ongoing stakeholder engagements with key representatives of the electricity sector that began in April 2021. Stakeholder engagement has included interviews, workshops, and working group meetings.
- Threshold-based analytics for asset-by-asset vulnerability assessments and updating design standards
- ‘Hourly climate profiles’ (for future time periods) as inputs into production cost, energy load forecasting, and other models
- Distribution of extreme temperature events to inform peak load, demand forecasts, and other applications
- Climate metrics and analytics to support long-range wildfire planning and management
- Using climate data to examine impacts on renewable energy generation and operations
Note: The description and composition of the applications might evolve or change as the project progresses.
About the notebooks
You may preview the following Jupyter notebooks containing example code created in support of these applications. Interactive notebooks are available through the Analytics Engine Jupyter Hub. The notebooks provide step-by-step functionality to access, analyze, and plot climate data available on the Analytics Engine. The notebooks can be used as-is or serve as a starting point to adapt to a specific organization’s needs, workflows, or particular applications.
Tools included in the notebooks provide examples for how to work with both the historical and projection data on the platform, and demonstrate how to move from the climate variables provided through the Analytics Engine to actionable information that can inform decision-making and risk assessments.
- Getting Started: explore and subset the data available on the Analytics Engine
- Threshold Tools Example: quantify frequency of extreme events
- Threshold Tools Applications: apply threshold tools to understand asset vulnerability
- Time-series Example: create and export time series of climate data
- Warming Levels: explore regional patterns at different global warming levels