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 …
represents 17% of the National Interconnected System. Due to the high level of uncertainty …
Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam
In hydroelectric plants, the responsibility for the operation of the reservoirs typically lies with
the national system operator, who controls the level of the reservoirs based on a stochastic …
the national system operator, who controls the level of the reservoirs based on a stochastic …
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 …
regarding external stimuli, and its signals compose a nonlinear dynamical system. There are …
Day-ahead electricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in NSW, Australia
YQ Tan, YX Shen, XY Yu, X Lu - Electric Power Systems Research, 2023 - Elsevier
Day-ahead electricity price forecasting plays a vital role in electricity markets under
liberalization and deregulation, which can provide references for participants in bidding …
liberalization and deregulation, which can provide references for participants in bidding …
Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on …
T Conte, R Oliveira - Energies, 2024 - mdpi.com
Global environmental impacts such as climate change require behavior from society that
aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels …
aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels …
Far beyond day-ahead with econometric models for electricity price forecasting
P Ghelasi, F Ziel - arXiv preprint arXiv:2406.00326, 2024 - arxiv.org
The surge in global energy prices during the recent energy crisis, which peaked in 2022,
has intensified the need for mid-term to long-term forecasting for hedging and valuation …
has intensified the need for mid-term to long-term forecasting for hedging and valuation …
[PDF][PDF] Artificial Intelligence and Signal Decomposition Approach Applied to Retail Sales Forecasting
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 …
such as planning and scheduling, resource management, marketing, logistics, and supply …
An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy.
D He, B Wang, X Gao, X Wang - Environmental & Earth …, 2021 - search.ebscohost.com
Aiming at the serious noise of bridge vibration signals in complex environment, this paper
proposed an adaptive filtering and denoising optimization method for bridge structural …
proposed an adaptive filtering and denoising optimization method for bridge structural …
[PDF][PDF] Selection of an Artificial Intelligence Forecasting Approach for PV Panel Prices
A Alhosani, S Amer, A Sleptchenko - ieomsociety.org
As the world moves towards renewable energy sources, solar energy is becoming more
widely recognized. This impacts photovoltaic (PV) panel prices, which requires deeper …
widely recognized. This impacts photovoltaic (PV) panel prices, which requires deeper …