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Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks
(2021)
Air pollution has been shown to have a direct effect on human health. In particular, PM2.5 has been proven to be related to cardiovascular and respiratory problems. Therefore, it is important to have accurate models to ...
Hydrological early warning system based on a deep learning runoff model coupled with a meteorological forecast
(MDPI AG, 2019)
© 2019 by the authors.The intensification of the hydrological cycle because of global warming raises concerns about future floods and their impact on large cities where exposure to these events has also increased. The ...
N-BEATS-RNN: Deep learning for time series forecasting
(2020-12-01)
This work presents N-BEATS-RNN, an extended version of an existing ensemble of deep learning networks for time series forecasting, N-BEATS. We apply a state-of-the-art Neural Architecture Search, based on a fast and efficient ...
Deep Learning Methods for Forecasting COVID-19 Time-Series Data: A Comparative Study
The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the ...
N-BEATS-RNN: deep learning for time series forecasting
(Universidade Federal de São CarlosUFSCarPrograma de Pós-Graduação em Ciência da Computação - PPGCC-SoCâmpus Sorocaba, 2021-01-27)
This work presents N-BEATS-RNN, an extended version of an ensemble of deep learning networks for time series forecasting, N-BEATS. We apply a state-of-the-art Neural Architecture Search, based on a fast and efficient ...
Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings
(2022)
Buildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely ...
Algorithms for forecasting cotton yield based on climatic parameters in Brazil
(2020-01-01)
Accurate forecasts of cotton yield are of great interest for the development of the market, increasing the sustainability of the sector worldwide. Thus, the objectives of this study were: 1) to evaluate the influence of ...
An Improved Deep Learning Model for Electricity Price Forecasting
Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically ...