A5044574416- Metaheuristic Optimization Algorithms Research
- Energy Load and Power Forecasting
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Heat Transfer and Optimization
- Electric Power System Optimization
- Neural Networks and Applications
- Grey System Theory Applications
- Optimal Power Flow Distribution
- Food Drying and Modeling
- Nanofluid Flow and Heat Transfer
- Machine Fault Diagnosis Techniques
- Heat Transfer Mechanisms
- Fluid Dynamics and Turbulent Flows
- Stock Market Forecasting Methods
- Anomaly Detection Techniques and Applications
- Building Energy and Comfort Optimization
- Fault Detection and Control Systems
- Power Systems and Renewable Energy
- Freezing and Crystallization Processes
- Advanced Control Systems Design
- Smart Grid Energy Management
- Advanced Algorithms and Applications
- Lattice Boltzmann Simulation Studies
- Antenna Design and Optimization
Universidade Federal do Paraná
2016-2025
Pontifícia Universidade Católica do Paraná
2015-2024
Universidade Federal do Pará
2016-2024
Universidade Tecnológica Federal do Paraná
2014-2019
Sardar Vallabhbhai National Institute of Technology Surat
2017
Ospedali Riuniti di Ancona
1992-2003
Institute for Magnetospheric Physics
1991
Evolutionary algorithms are heuristic methods that have yielded promising results for solving nonlinear, nondifferentiable, and multi-modal optimization problems in the power systems area. The differential evolution (DE) algorithm is an evolutionary uses a rather greedy less stochastic approach to problem than do classical algorithms, such as genetic programming, strategies. DE also incorporates efficient way of self-adapting mutation using small populations. potentialities its simple...
The significance of accurate short-term load forecasting (STLF) for modern power systems' efficient and secure operation is paramount. This task intricate due to cyclicity, non-stationarity, seasonality, nonlinear consumption time series data characteristics. rise accessibility in the industry has paved way machine learning (ML) models, which show potential enhance STLF accuracy. paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme (XGBoost), k-Nearest...
The cost of electricity and gas has a direct influence on the everyday routines people who rely these resources to keep their businesses running. However, value is strongly related spot market prices, arrival winter increased energy use owing demand for heating can lead an increase in prices. Approaches forecasting costs have been used recent years; however, existing models are not yet robust enough due competition, seasonal changes, other variables. More effective modeling approaches...
Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise conductivity and increase leakage current until a flashover occurs. To improve reliability electrical power system, it is possible evaluate development fault in relation thus predict whether shutdown may occur. This paper proposes use empirical wavelet transform (EWT) reduce influence non-representative variations combines attention mechanism with long short-term memory (LSTM)...
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce costs, increase efficiency, and minimize downtime. In this paper, the predicting machine failures by possible anomalies in data addressed through time series analysis. are from sensor attached to an (motor) measuring vibration variations three axes: X (axial), Y (radial), Z (radial X). dataset used train hybrid convolutional neural network with long short-term memory (CNN-LSTM)...
Electrical power grid insulators installed outdoors are exposed to environmental conditions, such as the accumulation of contaminants on their surface. The increase surface conductivity insulators, increasing leakage current until there is a flashover. Evaluating in relation contamination level one way determine insulation condition. This paper evaluates time series from high-voltage laboratory experiment using porcelain pin-type insulators. Time forecasting performed with collection machine...
This paper addresses the challenge of predicting dam level rise in hydroelectric power plants during floods and proposes a solution using an automatic hyperparameters tuning temporal fusion transformer (AutoTFT) model. Hydroelectric play critical role long-term energy planning, accurate prediction is crucial for maintaining operational safety optimizing generation. The AutoTFT model applied to analyze time series data representing water storage capacity plant, providing valuable insights...