Predicting wind energy generation with recurrent neural networks

Decarbonizing the energy supply requires extensive use of renewable generation. Their intermittent nature requires to obtain accurate forecasts of future generation, at short, mid and long term. Wind Energy generation prediction is based on the ability to forecast wind intensity. This problem has been approached using two families of methods one based on weather forecasting input (Numerical Weather Model Prediction) and the other based on past observations (time series forecasting). This work deals with the application of Deep Learning to wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting in a 12 h ahead prediction. For the Time Series input we used the US National Renewable Energy Laboratory’s WIND Dataset [3], (the largest available wind and energy dataset with over 120,000 physical wind sites), this dataset is evenly spread across all the North America geography which has allowed us to obtain conclusions on the relationship between physical site complexity and forecast accuracy. In the preliminary results of this work it can be seen a relationship between the error (measured as R2R2 ) and the complexity of the terrain, and a better accuracy score by some Recurrent Neural Network Architectures.Peer ReviewedPostprint (author's final draft

Ideal vs. Non‐ideal Theory: A Conceptual Map

This article provides a conceptual map of the debate on ideal and non‐ideal theory. It argues that this debate encompasses a number of different questions, which have not been kept sufficiently separate in the literature. In particular, the article distinguishes between the following three interpretations of the ‘ideal vs. non‐ideal theory’ contrast: full compliance vs. partial compliance theory; utopian vs. realistic theory; end‐state vs. transitional theory. The article advances critical reflections on each of these sub‐debates, and highlights areas for future research in the field

Ideal matrices. III.

In this paper ideal matrices with respect to ideals in the maximal order of an algebraic number field are connected with the different of the field and with group matrices in the case of normal fields whose maximal order has a normal basis

Dostoevsky’s Ideal Man

This paper aimed to provide a comprehensive examination of the ideal Dostoevsky human being. Through comparison of various characters and concepts found in his texts, a kenotic individual, one who is undifferentiated in their love for all of God\u27s creation, was found to be the ultimate to which Dostoevsky believed man could ascend

Motivation by Ideal

I offer an account of how ideals motivate us. My account suggests that although emulating an ideal is often rational, it can lead us to do irrational things