Weather holds significant sway over the renewable energy sector, shaping both the demand for energy and the production processes involved.
Recognizing and understanding the intertwined relationship between weather patterns and energy dynamics is crucial for effective energy planning, resource allocation, and to maintain sustainable development.
We dived into the interplay between weather and the energy industry, exploring how hyper local and extremely accurate weather intelligence benefit the renewable energy industry.
Weather’s Impact on Renewable Energy Power Production Forecasting
Wind Power
Wind energy production is directly impacted by wind speed and consistency. Turbine performance is influenced by variations in wind patterns caused by weather systems. Accurate forecasting of wind speed, direction, and turbulence helps optimize power production, plan maintenance, and balance the grid.
Solar Power
Solar energy production heavily relies on the availability of sunlight. Cloud cover, fog, and seasonal variations affect the amount of sunlight reaching solar panels. Precise solar forecasting helps grid operators manage fluctuations, integrate solar power efficiently, and forecast peak generation periods.
Hydroelectric Power
Weather patterns, particularly rainfall and snowmelt, significantly affect the availability of water for hydropower generation. Hydrological models that consider weather forecasts enable operators to optimize reservoir levels, manage water releases, and ensure consistent power production.
Leveraging Buluttan’s Weather Intelligence
In addition to planning and resource management, renewable energy firms pay imbalance penalties due to inaccurate power generation forecasts that reach up to $50.000 for a wind farm with 60 MW installed capacity.
With a sophisticated algorithm, we can forecast the renewable energy power production amounts in wind and solar energy, where weather is the #1 factor affecting the output. Helping our partners in the renewable energy industry by reducing the imbalance penalties from 8% to 15%, iteratively and helping them better predict the direction of the energy market tomorrow.
