Renewcast is a platform that employs predictive algorithms to forecast renewable energy generation and market trends. By providing energy producers and investors with precise data, it enhances decision-making and optimizes resource allocation in the renewable energy sector.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SPFounders
Product
Problem
Renewable energy producers and investors face challenges in accurately predicting energy generation due to the variability of weather conditions and other factors. Inaccurate forecasting leads to inefficient resource allocation, increased imbalance costs, and lost revenue opportunities. Managing the complexity of data from various sources further exacerbates these challenges.
Solution
Renewcast offers a forecasting-as-a-service platform that provides short-term and intra-day predictions for wind and solar power generation. The platform leverages proprietary deep learning models, combined with advanced data pipeline management and meteo data forecast optimization, to create a digital twin of each turbine. This digital twin technology enables precise modeling of wind and turbine behavior, resulting in forecasting errors as low as 7% nMAE, which is significantly better than the global market benchmark. By providing accurate forecasts, Renewcast helps energy operators reduce imbalance costs by up to 30% and enhance overall profitability.
Target Audience
Renewcast primarily targets renewable energy producers, energy operators, and investors who require accurate and reliable forecasting for wind and solar power generation.
Features
- Intra-day, day-ahead, 48-hour, and medium-term (up to 15 days) forecasting for wind and solar power generation
- API access for direct retrieval of forecasts
- Hourly forecast updates
- Client UI with parametric report configuration and data download capabilities
- Azure cloud-based platform for scalability and reliability
- Proprietary deep learning models for accurate forecasting
- Meteo data forecast optimization for enhanced precision
- Turbine data correction and enrichment for improved model accuracy
- Digital twin technology to model the behavior of wind and turbines