AI-Driven Solar & Wind Power Forecasting and Scheduling

Project Snapshot

• Industry: Renewable Energy (Solar & Wind Power)

• Client Type: Renewable Energy Provider

• Duration: Multi-phase implementation across multiple sites

• Deployment Model: Cloud-based data pipelines & forecasting dashboards

• Technologies: NASA Meteorological Data, Python, Machine Learning, Deep Learning (LSTM, ARIMA, Decision Tree, SVM, Random Forest, Linear Regression), Tableau

The Challenge

Renewable energy providers face constant challenges in balancing accurate forecasting, scheduling, and reporting across multiple solar and wind power generation sites. Key difficulties included:

• Limited accuracy in power generation forecasting, leading to operational inefficiencies and penalties

• Need for timely, multi-frequency reporting (hourly, daily, weekly, monthly, quarterly, annual) to support leadership decision-making

• Complexity in handling and transforming large-scale meteorological datasets into actionable insights

• Requirement for intraday forecasting updates every 15 minutes to improve responsiveness to weather variability

• Lack of intuitive dashboards and analytics tools for penalty analysis and performance monitoring

Our Solution

We developed a comprehensive AI/ML-based forecasting and scheduling system to address these challenges:

• Meteorological Data Acquisition & Processing

  • Automated data retrieval from NASA sources

  • Pre- and post-processing pipelines to convert .NC datasets into structured CSV formats for further analysis

• AI & ML Forecasting Models

  • Designed and deployed advanced time-series forecasting models including Decision Tree, Random Forest, SVM, Linear Regression, ARIMA, and LSTM

  • Implemented ensemble approaches for higher accuracy in solar and wind power predictions

• Intraday Forecasting

  • Built a 15-minute interval forecasting system, ensuring near real-time updates to improve scheduling efficiency

• Multi-Site Forecasting

  • Scaled the solution to support multiple renewable energy sites simultaneously, with consistent forecasting performance across geographies

• Analytics & Visualization

  • Developed interactive dashboards in Tableau and Python for penalty data analysis, forecasting accuracy tracking, and leadership reporting

• Automated Reporting System

  • Delivered reports on hourly, daily, weekly, monthly, quarterly, and annual intervals, aligned with operational and analytical needs

The Impact

The AI-driven forecasting solution delivered significant measurable benefits:

• Improved Forecast Accuracy → Enabled more reliable scheduling and reduced penalties

• 15-Minute Forecast Updates → Enhanced intraday responsiveness to changing weather patterns

• Automated Multi-Level Reporting → Provided leadership with actionable insights across different time horizons

• Operational Efficiency → Reduced manual workload and streamlined the forecasting process across sites

• Cost Optimization → Lowered penalty costs through precise intraday forecasting and improved scheduling accuracy

Our Role

Our team partnered with the client to:

• Build data pipelines for large-scale meteorological data

• Engineer AI/ML and deep learning models for renewable energy forecasting

• Deploy real-time intraday forecasting systems

• Create visual analytics dashboards in Tableau and Python

• Automate multi-frequency reporting systems for business leaders

Client Testimonial

“The AI-powered forecasting platform has transformed the way we schedule and manage renewable energy. Accurate intraday updates and automated reporting have significantly improved efficiency and reduced operational penalties.”
— Head of Renewable Energy Operations