What is the difference between Deep Learning and statistical methods in energy production forecasting? 


 

The ability to accurately predict renewable energy production is essential for optimizing its integration into power grids and improving energy efficiency. However, it is a process exposed to a high level of uncertainty and volatility due to weather conditions. So far, most systems are based on a combination of meteorological data and statistical methods to predict the generation of energy from renewable parks. However, these traditional methods used for energy production prediction have certain limitations in terms of accuracy. These problems can be solved with Deep Learning models capable of learning short and long-term temporal relationships. 

 

While statistical methods have proven effective in stable environments, the counterpoint is found when they must deal with weather predictions that are not linear. This fact can pose a challenge in territories like Spain, given its geographical particularities both in the peninsula and in the insular territories. The climatic diversity, with significant variations in climate between different regions and the presence of microclimates, makes statistical models generate less accurate and reliable predictions. 

 

Artificial Intelligence for More Accurate Predictions 

 

Faced with these limitations, Deep Learning models offer a reliable alternative. Their ability to process large volumes of data and learn from it allows these models to identify complex patterns and short and long-term temporal relationships between input data, which is crucial for predicting energy production under changing weather conditions. The data ends up becoming a constantly training learning algorithm to accurately predict or estimate the production of renewable energy parks. 

 

This new method related to the prediction of renewable energy production has a great impact on energy planning and management. Therefore, at Ravenwits using neural networks, our methodology is capable of processing and analyzing large volumes of meteorological and energy production data, offering highly accurate predictions that are crucial for the prediction of renewable energy production. 

 

Unlike statistical methods, which can fall short in scenarios of high volatility and complexity, Ravenwits' solutions dynamically adapt to changing conditions, improving the efficiency and reliability of energy predictions. This not only increases the competitiveness of renewable energy plants but also contributes to a more sustainable and efficient management of energy resources. 

 

Red Eléctrica Española Collaboration 

 

The results obtained by Ravenwits in its collaboration with Red Eléctrica Española since 2019 have achieved significant improvements in predictions, reducing errors by 5% for wind energy in Spain and making even greater advances in solar prediction. This approach has led to the integration of its predictors into REE's systems, highlighting the potential of Deep Learning to transform renewable energy prediction. 


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