Hybrid filter–wrapper feature selection for short-term load forecasting
Selection of input features plays an important role in developing models for short-term load
forecasting (STLF). Previous studies along this line of research have focused pre-dominantly …
forecasting (STLF). Previous studies along this line of research have focused pre-dominantly …
Deep neural networks for ultra-short-term wind forecasting
The aim of this paper is to present input variable selection algorithm and deep neural
networks application to ultra-short-term wind prediction. Shallow and deep neural networks …
networks application to ultra-short-term wind prediction. Shallow and deep neural networks …
Selection of temporal lags for predicting riverflow series from hydroelectric plants using variable selection methods
The forecasting of monthly seasonal streamflow time series is an important issue for
countries where hydroelectric plants contribute significantly to electric power generation …
countries where hydroelectric plants contribute significantly to electric power generation …
Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures
In this paper, we perform the first comparison of a large variety of effective connectivity
measures in detecting causal effects among observed interacting systems based on their …
measures in detecting causal effects among observed interacting systems based on their …
Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting
This paper presents a data-driven approach applied to the long term prediction of daily time
series in the Neural Forecasting Competition. The proposal comprises the use of adaptive …
series in the Neural Forecasting Competition. The proposal comprises the use of adaptive …
Soft sensor development based on the hierarchical ensemble of Gaussian process regression models for nonlinear and non-Gaussian chemical processes
L Wang, H Jin, X Chen, J Dai, K Yang… - Industrial & …, 2016 - ACS Publications
Chemical processes are often characterized by nonlinearity, non-Gaussianity, shifting
modes, and inherent uncertainty that pose significant challenges for accurate quality …
modes, and inherent uncertainty that pose significant challenges for accurate quality …
A multifactorial framework for short-term load forecasting system as well as the jinan's case study
Y Gao, Y Fang, H Dong, Y Kong - IEEE Access, 2020 - ieeexplore.ieee.org
Accurate and reliable short-term electric load forecasting (STLF) plays a critical role in power
system to enhance its routine management efficiency and reduce operational costs …
system to enhance its routine management efficiency and reduce operational costs …
Adaptive predictive power management for mobile LTE devices
P Brand, J Falk, JA Sue, J Brendel… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Reducing the energy consumption of mobile phones is a crucial design goal for cellular
modem solutions for LTE and 5G NR standards. Most dynamic power management …
modem solutions for LTE and 5G NR standards. Most dynamic power management …
Técnica de identificação de modelos lineares e não-lineares de séries temporais
Este trabalho apresenta uma proposta de identificação de modelos de séries temporais
baseada no algoritmo de Informação Mútua Parcial (PMI). Este critério leva em conta tanto …
baseada no algoritmo de Informação Mútua Parcial (PMI). Este critério leva em conta tanto …
An adaptive hybrid model for monthly streamflow forecasting
This paper suggests a new algorithm for generating Takagi-Sugeno fuzzy systems applied
for time series prediction. The model proposed comprises two phases. First, the model …
for time series prediction. The model proposed comprises two phases. First, the model …