Grid operators face the challenge of providing spare capacity to back up the variable supply of renewable energy. At the same time, they want to minimize excess capacity and reduce the cost of balancing the grid.
Moata Solar Yield Forecaster gives operators access to accurate supply forecasts. It's an AI-based tool specifically designed to forecast the performance of large, complex, or multi-site portfolios.
With more accurate forecasting from solar photovoltaic (PV) plants, project owners can sell their output "subsidy free" or via Power Purchase to balance their output. Grid operators can balance the grid and asset managers can optimize the dispatch of their assets and determine the reasons for underperformance — all in real time.
- Energy yield modeling handles complex PV plants, including irregular layouts and novel technologies such as bifacial or floating solar.
- Model architecture covers historical and current performance, and full range of forecast time horizons.
- Solution can be combined with demand forecasting modeling to produce whole system supply or demand balance forecasts.
- Fully automated platform, aggregating data to deliver a single source of truth.
- State-of-the-art forecasting analytics, with commercial-grade weather forecasts at different horizons (up to 10 days ahead) and granularities, for multiple applications and global coverage.
- Forecasting accuracy reporting for continual improvement.
- Customizable solution that can evolve with client requirements.
- Combination of AI models with physical performance models to optimize performance.
- Cost-efficient grid balancing from improved forecasting accuracy.
- Reduced data handling requirements and reduced human error with implementation of systematic and best practice data management.
- An end to manual processes, with insights instantly enabling information to be acted on fast.
- Accelerated operational reporting and quicker decision-making with instant access to data and real-time updates.