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Related Concept Videos

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Three-Winding Transformers01:19

Three-Winding Transformers

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Related Experiment Videos

Short-term load forecasting using a metaheuristic optimized temporal fusion transformer with decomposition technique.

Radhika Chandrasekaran1, Senthil Kumar Paramasivan1

  • 1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Frontiers in Artificial Intelligence
|May 27, 2026
PubMed
Summary

This study introduces an advanced short-term load forecasting model using Temporal Fusion Transformer (TFT) with Multivariate Variational Mode Decomposition (MVMD) and GOAT Optimization Algorithm (GOA). The novel approach enhances accuracy by effectively handling complex energy data patterns.

Keywords:
attention mechanismdecompositiondeep learninggated residual networkoptimizationtemporal fusion transformer

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Short-term load forecasting is crucial for energy grid stability, but faces challenges from non-stationary, non-linear load patterns influenced by weather and consumption.
  • Traditional statistical methods and machine learning models struggle with complex temporal dependencies and long-range patterns in energy data.

Purpose of the Study:

  • To develop a robust and accurate short-term load forecasting model capable of addressing the complexities of multivariate energy data.
  • To leverage deep learning, specifically the Temporal Fusion Transformer (TFT), for enhanced feature extraction and prediction accuracy.

Main Methods:

  • A novel forecasting framework combining Multivariate Variational Mode Decomposition (MVMD) for data preprocessing and Temporal Fusion Transformer (TFT) for modeling.
  • Optimization of the TFT model's hyperparameters using the GOAT Optimization Algorithm (GOA) to improve performance.
  • Evaluation of model accuracy using standard metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (sMAPE).

Main Results:

  • The proposed MVMD-TFT-GOA model demonstrated superior performance compared to existing forecasting models.
  • MVMD effectively decomposed complex load data into simpler components, reducing noise and enhancing feature extraction for the TFT.
  • SHapley Additive exPlanation (SHAP) analysis provided insights into feature importance for model predictions.

Conclusions:

  • The integrated MVMD-TFT-GOA approach offers a significant advancement in short-term load forecasting accuracy and reliability.
  • The model's ability to handle non-stationary, non-linear data and long-range dependencies makes it suitable for real-world energy management applications.
  • The interpretability offered by SHAP analysis enhances trust and understanding of the forecasting model.