Advanced data analysis and visualization for photovoltaic performance insights
Interactive tools for comprehensive performance analysis
Access live performance data, historical trends, and predictive analytics through our interactive visualization platform. Filter by region, installation type, time period, and performance metrics.
Real-time generation data updated every minute from active installations nationwide.
Filter data by province, capacity range, installation type, and date ranges.
Download reports in CSV, JSON, or PDF format for detailed analysis.
Predictive models for solar generation planning
Neural network algorithms trained on historical performance data predict generation output 1-7 days ahead. Models account for weather forecasts, seasonal patterns, and installation-specific characteristics. Accuracy: 85-92% for day-ahead predictions.
Forecast models integrate meteorological data including cloud cover predictions, temperature forecasts, precipitation probability, and wind patterns. Weather service APIs update hourly for continuously refined predictions.
Uncertainty quantification provides confidence intervals for predictions. P10, P50, and P90 forecast scenarios enable risk assessment and planning for grid operators and energy managers.
Multi-year performance trends showing seasonal variations and long-term capacity factor stability
Identifying performance trends across temporal scales
Sun path analysis, shading identification, and peak production timing. Identifies morning/afternoon asymmetries indicating orientation issues or localized obstructions.
Seasonal irradiance variations, temperature coefficient impacts, and soiling accumulation patterns. Comparison with long-term averages highlights anomalous months.
Year-over-year performance changes, degradation rate calculation, and capacity factor stability. Multi-year analysis separates weather variability from equipment aging.
Automated algorithms continuously monitor performance data to identify deviations from expected behavior:
Detection thresholds are dynamically adjusted based on historical variability and installation-specific characteristics to minimize false positives while ensuring genuine issues are flagged promptly.
Quantifying factors reducing energy output
DC wiring resistance, inverter conversion inefficiency, transformer losses. Typical: 5-8% of theoretical output.
Obstructions from buildings, trees, adjacent rows. Varies with sun angle, time of day. Identified through string-level monitoring.
Dust, bird droppings, pollen accumulation. Geographic and seasonal variation. 2-8% typical, higher in agricultural areas.
Winter-specific for Canadian installations. Depends on tilt angle, snow persistence, ambient temperature. Can reach 20-40% in northern regions December-February.
8 chart types for comprehensive data exploration
Time-series data showing trends, seasonal variations, and long-term patterns in generation output.
Comparative analysis across installations, regions, or time periods. Monthly aggregations and year-over-year comparisons.
Stacked visualizations showing contribution of multiple arrays or cumulative generation over time.
Proportional breakdown of capacity by province, installation type, or contribution to total generation.
Correlation analysis between irradiance and output, temperature and efficiency, or other performance relationships.
Hour-by-month performance matrices showing peak generation times and seasonal intensity variations.
Real-time performance ratio indicators, capacity factor displays, and efficiency meters for quick assessment.
Directional data visualization for orientation analysis, sun path mapping, and azimuth-dependent performance.
Raw data in comma-separated format for spreadsheet analysis, statistical software, or custom processing scripts.
Structured data format for API integration, web applications, and programmatic access to performance metrics.
Formatted performance reports with charts, summary statistics, and analysis commentary for presentations and documentation.