Energy Analytics · Renewable Forecasting
Weather-aware time-series forecasting for solar power generation and grid planning.
Renewable energy production, particularly solar power, is highly dependent on weather conditions such as solar irradiation and temperature. Grid operators and energy providers must anticipate fluctuations in power generation to ensure reliable electricity supply and effective energy planning.
Without accurate forecasting, sudden changes in solar generation can create imbalances between energy supply and demand, increasing operational risks for power grids. The challenge is to develop a forecasting approach that captures daily solar production cycles and incorporates weather information to improve prediction accuracy.
A time-series forecasting approach was used to predict solar power generation based on historical production data and weather variables. The generation dataset was merged with environmental measurements such as solar irradiation and temperature to capture the influence of weather conditions on energy output. An ARIMAX model was applied to incorporate both temporal dependencies and exogenous weather factors, enabling more accurate forecasting of daily solar generation patterns.
