Machine Learning Solar Power Forecasting
Meteo-Logic offers a unique solution specifically tailored to provide the solar-generated energy industry with highly accurate, localized, short-term power forecasting.
Meteo-Logic’s Main Advantages
- High level of accuracy and reliability
- Notification system
- Flexible day-ahead forecast mechanism
- Seamless integration
- User-friendly interface
- Fully automated system
- Online updates around the clock
- Affordable pricing
In a world where the sun’s path, combined wi...
In a world where the sun’s path, combined with atmospheric conditions, makes forecasting very tricky, Meteo-Logic reduces risk and delivers more reliable forecasts by offering groundbreaking, accurate predictions for solar farm power generation, based on the unique and proprietary MLF approach.
Rather than building complex and inherently inaccurate models from global weather models all the way down to the panels, Meteo-Logic’s MLF technology adopts a unique approach, assuming that making the correlation between the synoptic situation and measured values on the ground requires a special, complex model for every forecasting point. Meteo-Logic’s outstanding prediction algorithm, based on Bayesian statistics, enables constantly updated, accurate power and weather forecasts for a specific location.
Meteo-Logic provides 5-day ahead hourly forecasts, which dramatically reduce financial uncertainty while maximizing profits and reducing direct and indirect expenses.
Accurate power forecasting allows operators to comply with the relevant utility SLA and avoid penalties arising from discrepancies between promised and actual delivery.
Weather forecasting is also available to track temperature, humidity, wind speed, wind direction and rain.
- Solar Power forecast per site or per group of panels
- Weather forecast, all relevant parameters
- 5-day ahead, hourly forecast
- Customized notifications for operational and maintenance use
- Personalized Day Ahead reports
Meteo-Logic provides an ongoing ranking system using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to evaluate the forecast performance, comparing measured power generation with the predicted forecast.
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