The production of energy by solar or wind farms highly depends on the weather conditions and therefore fluctuates significantly over time. Any increase in the production of these renewable sources has to be foreseen in order to avoid wastes. On the contrary, any decrease in the production has to be compensated by fossil energy. As an anticipated purchase of energy enables to get lower prices and to avoid wastes, the challenge is therefore to accurately predict the future production of solar and wind farms to better integrate them in the power grid.
The weather directly impacts some energy needs. In winter for example, cold temperatures are responsible for the peaks of demand for electricity or natural gas for heating. Forecasting the energy demand is an important challenge in order to adjust the production, optimize the distribution and avoid power failures.
By exploiting weather forecast from numerical models in conjunction with actual power productions,
the predictive analytics techniques enable to extract accurate models for the short term prediction of
solar and wind energy production. It can also be used to find the best places for future installations
of solar or wind farms.
With historical data of the energy consumption and the weather conditions, predictive analytics can
build models to anticipate the future demand.