The increasing integration of renewable energies into modern electricity grids is creating new challenges for grid operators and

energy traders. Wind and solar energy are highly dependent on the weather and are therefore particularly characterized by spa-

tial and temporal fluctuations. Therefore, precise and reliable power forecasts for hours and days into the future are essential

for grid stability, market optimization, and cost reduction. Moreover, the increasing availability and quality of power measure-

ments enables a variety of methods to adapt weather and power forecasts to these measurements, with combining these diverse

approaches often leading to improved accuracy. However,in an environment with changing conditions, such as additional con-

struction of wind turbines or different regularization of the grid operators, past behaviour can become outdated quickly, adaptive

combination offers the potential for further enhanced forecasting accuracy and reliability. Therefore, we present an adaptive

combination method that produces a combined forecast based on power forecasts of different weather models. Next to the usual

predictors, we use additionally spatio and temporal features. This approach is applied to different baseline and Machine Learning

models. We compare online machine learning methods such as Online Sequential Extreme Learning Machines (OsELMs) that

adaptively combine predictions with other benchmark approaches and individual forecasting models. One particular focus is on

the systematic analysis of defined update intervals (daily, weekly, monthly). During each interval, the adaptive learning process

accumulates new measurement and forecast data and adapts its weights automatically in order to optimize the prediction quality.

Both the combination and the adaptive training of the ML models show that the updates lead to improved forecasting accuracy.