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Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting

A wind power forecast is an useful support tool for planning and operating wind farm production, facilitating decisions regarding maintenance and load share. This paper presents an evaluation of a cooperative method, which uses a time series …

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can …

Cooperative ensemble learning model improves electric short-term load forecasting

Efficient models for short-term load forecasting (STLF) plays a crucial role in establishing the companies’ energetic planning due to their importance in electric power distribution and generation systems. An ensemble learning model based on dual …

Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach

Wind energy is an emerging source of renewable energy in Brazil. Nevertheless, it already accounts for 17% of the National Interconnected Network. Due to the great intricacy of wind speed variations, it is difficult to predict wind energy with high …

Discrete differential evolution metaheuristics for permutation flow shop scheduling problems

Scheduling problems (SP) in the permutation flow shop (PFS) environment are present in many intermittent production industries, consisting of to determinate the processing order of n jobs in m sequential machines, with the purpose to optimize some …

Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting

The use of wind energy plays a vital role in society owing to its economic and environmental importance. Knowing the wind power generation within a specific time window is useful for facilitating decision making in terms of maintenance, electricity …

A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting

Wind energy is one of the sources which is still in development in Brazil. However, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, predicting wind energy with high …

Inteligência artificial como suporte a tomada de decisão para previsão de novos casos do coronavírus (COVID-19)

Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current …

Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

The intermittent nature of wind can represent an obstacle to get reliable wind speed forecasting, thus many methods were developed to improve the accuracy, due to unstable behavior patterns and the presence of noise signal. In order to overcome this …