Can DeepMind predict wind power output 36 hours in advance?

Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance.

How to predict future wind speed?

Generally, different models need to be configured according to the characteristics of each sub-sequence to predict the future wind speed more accurately. Among them, prediction methods, such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most commonly used models (Wu and Xiao, 2019).

Can we predict wind energy levels 48 hours in advance?

The researchers' method was able to predict wind energy levels 48 hours in advance and provide useful forecasts for wind energy (Sideratos and Hatziargyriou, 2007). Kariniotakis and colleagues developed models using fuzzy logic and recurrent high-dimensional neural networks to predict the power of a wind farm.

How has wind power forecasting evolved?

Special attention is given to short-term forecasting, crucial for the day-ahead electricity market. This study traces the evolution of wind power forecasting, from early statistical approaches to the integration of numerical weather prediction, machine learning, neural networks, and advanced techniques.

How can Ann predict wind power generation?

It excels by leveraging computational algorithms to discern complex patterns, leading to more nuanced and dynamic predictions of wind power generation (Demolli et al., 2019, Louka et al., 2008b). ANNs are computational models that are inspired by the human brain.

Can wind power plant data be used to predict wind speed?

However, as wind power plant technology becomes more sophisticated, an increasing amount of unstructured data, such as motor temperature data, and satellite image data becomes available. Currently, there is little research on such data, but such data have great potential application value for effective and timely forecasting of wind speed.