Smart iPV DASHBOARD – MONITOR & FORECAST SOLUTION FOR INDUSTRIAL PV POWER PLANTS
WEB APPLICATION
- Integrates data from: inverters, smart meters, IBD, weather sources (local stations or sensors and web providers).
- Provides decision support tools for monitoring, forecast and analytics.
- Forecast PV power for 7 days at 15-minute.
- Fully customized web application.
POWER PLANT MONITORING
- Collect and process data from smart meters, inverters and IBD.
- Monitor PV generation to detect possible power loss, inverter and grid connection issues.
- KPI analytics: yield, Energy Performance Index (EPI), Power Performance Index (PPI), Performance Ratio (PR), Partial Power Loss Indicator (PPLI), Partial Energy Loss Indicator (PELI)
POWER PLANT FORECAST
- Powerful Artificial Intelligence forecast that combines multiple weather sources to provide 15-minute forecast for 7 days.
- Multiple forecast scenarios based on weather probability.
- View tabular data and export in .csv or excel for different time intervals.
- Monitor and forecast the PV power for each inverter.
IMBALANCE FORECAST
- Forecast the deficit or surplus of the system using 3 powerful AI models.
- Each model provides the forecast probability.
WEATHER DATA
- Collect and integrate more than 10 weather sources related to the location of the PV power plant.
- Weather monitoring and forecast for different time intervals and weather stations.
All we need from you to CUSTOMIZE the APP
- PV power plant info: location (latitude, longitude), rated power
- IBD data for the last 2-3 months
- For more advanced monitoring and analysis: inverters or smart meters data
Our solution was appreciated and published in prestigious international journals:
- S.V. Oprea, Bara, A – A Stacked Ensemble Forecast for Photovoltaic Power Plants combining Deterministic and Stochastic Methods, Applied Soft Computing (Q1), Volume 147, Published: November 2023, https://doi.org/10.1016/j.asoc.2023.110781
- A Bâra, S-V Oprea, Embedding the Weather Prediction Errors (WPE) into the PV Forecasting Method using Deep Learning. Journal of Forecasting, Wiley, https://doi.org/10.1002/for.3064
- S-V Oprea, A Bâra, On-grid and Off-grid Photovoltaic Systems Forecasting using a Hybrid Meta-learning Method, Knowledge and Information Systems, https://doi.org/10.1007/s10115-023-02037-8
- S.V. Oprea, Bara, A – Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies – PV Agigea and PV Giurgiu located in Romania, Computers in Industry (Q1), Volume: 120, Published: September 2020. https://doi.org/10.1016/j.compind.2020.103230