A Comparative Analysis of Deep Neural Networks and Gradient Boosting Algorithms in Long-Term Wind Power Forecasting - Ivanovic, Luka; Milić, Saša; Sokolović, Živko; Rakić, Aleksandar (Proceedings, Electrical engineering institute "Nikola Tesla", 2024)
A vital step toward a sustainable future is the power grid’s incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network (RNN) algorithms and gradient boosting algorithms (GBMs) applied to time series data for the regression issue of estimating the active power generated by a wind farm (WF). GBM algorithms combine the advantages of a few machine learning models (decision trees, random forests, etc.) to produce a powerful prediction model. In addition to existing conventional RNN, the article deals with long short-term memory LSTM (LSTM) and gate ruccerrent unit (GRU) as cut of the edge models for time series prediction. In addition to conventional RNNs, the article deals with long short-term memory LSTM (LSTM) and gated recurrent unit (GRU) as cutting-edge models for time series analysis and predictions. A comprehensive analysis was carried out on a large wind power generation data set.