Forecasting

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 …

Forecasting COVID-19 pandemic using an echo state neural network-based framework

Forecasts can help in the decision-making process. Epidemiological forecasts are no different, they can help to evaluate the scenario and possible direction of disease spread, for guiding possible interventions. In this work, Echo State Networks …

Seasonal-trend and multiobjective ensemble learning model for water consumption forecasting

Water consumption forecasting is essential for the development of efficient city planning. Due to the non-linearities and relations of the water consumption with different factors the development of an accurate forecasting system is challenging. This …

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 …

Forecasting the cumulative cases of COVID-19 in four large Brazilian cities using machine learning approaches

The Coronavirus disease 2019 (COVID-19) is a disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow knowing the number of future COVID-19 cases. In …

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 …

Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

The development of accurate models to forecast electricity energy prices is a challenge due to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage approach is proposed to forecast multi-stepahead (one, two and …

Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods

Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the …

Solar Power Forecasting Based on Ensemble Learning Methods

Alternative energy sources are becoming more and more common around the world. In order to reduce environmental pollution and CO 2 emissions, in addition to being an ideal solution to overcome the energy crisis. In this context, power energy stands …