This study proposes hybrid machine learning (ML) models to predict the daily urban water consumption scenario in a neighborhood Brazilian city. The framework evaluates various signal decomposition modes, including empirical wavelet transform (EWT), …
The wind energy forecast is an useful tool for wind farm production planning, and operation, facilitating decision making in terms of maintenance, electricity market clearing, and load sharing. This study proposes a cooperative ensemble learning …
Sales forecasting is essential for decision-making and are crucial in many areas of a firm, such as planning and scheduling, resource management, marketing, logistics, and supply chain. Due to the fluctuations in retail sales, prediction with high …
Dengue is considered a public health problem in tropical regions, periodically affecting an increasing number of citizens. Consequently, the development of efficient models is essentials to short and long-term forecasting, supporting health care …
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 …
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 …
The development and expansion of clean energy, such as wind energy, are important in the preservation of the environment and development of local economies and an alternative to hydroelectric and thermal energies. In this respect, the development of …
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, prediction of wind speed with …
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 …
The objective of this paper is to develop an efficient ensemble learning model to predict the heating load (HL) and the cooling load (CL) of residential buildings, considering eight input variables (relative compactness, surface area, wall area, roof …