Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition

Energy, Год журнала: 2024, Номер 292, С. 130493 - 130493

Опубликована: Янв. 27, 2024

Язык: Английский

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Hypertuned temporal fusion transformer for multi-horizon time series forecasting of dam level in hydroelectric power plants

International Journal of Electrical Power & Energy Systems, Год журнала: 2024, Номер 157, С. 109876 - 109876

Опубликована: Фев. 21, 2024

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 decision-making emergency situations. results demonstrate that surpasses other deep learning approaches, achieving high accuracy across different horizons. Having root mean square error (RMSE) 2.78×10−3 short-term forecasting 1.72 considering median-term forecasting, shows be promising presented this paper. had lower RMSE than adaptive neuro-fuzzy inference system, long memory, bootstrap aggregation (bagged), sequential (boosted), stacked generalization ensemble approaches. findings underscore potential improving efficiency, ensuring safety, generation flood events.

Язык: Английский

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Customer churn prediction in imbalanced datasets with resampling methods: A comparative study

Expert Systems with Applications, Год журнала: 2024, Номер 246, С. 123086 - 123086

Опубликована: Янв. 13, 2024

Язык: Английский

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Evaluation of visible contamination on power grid insulators using convolutional neural networks

Electrical Engineering, Год журнала: 2023, Номер 105(6), С. 3881 - 3894

Опубликована: Июль 14, 2023

Язык: Английский

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Hypertuned-YOLO for interpretable distribution power grid fault location based on EigenCAM

Ain Shams Engineering Journal, Год журнала: 2024, Номер 15(6), С. 102722 - 102722

Опубликована: Март 1, 2024

Ensuring the reliability of electrical distribution networks is a pressing concern, especially given power outages due to surface contamination on insulating components. Surface can elevate conductivity, thereby resulting in failures that lead shutdowns. Addressing this challenge, paper proposes an approach for real-time monitoring grids prevent such incidents. A hypertuned version you only look once (YOLO) model tailored application. We refine model's hyperparameters by integrating genetic algorithm maximize its detection performance. The EigenCAM technique enhances visual interpretability outcomes, providing operators with actionable insights maintenance and tasks. Benchmark tests reveal proposed Hypertuned-YOLO outperforms Detectron (Masked R-CNN), YOLOv5, YOLOv7 models. achieves F1-score 0.867 [email protected] 0.922, validating robustness efficacy.

Язык: Английский

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One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

Expert Systems with Applications, Год журнала: 2024, Номер 252, С. 124154 - 124154

Опубликована: Май 7, 2024

Язык: Английский

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Seq2Seq-LSTM With Attention for Electricity Load Forecasting in Brazil

IEEE Access, Год журнала: 2024, Номер 12, С. 30020 - 30029

Опубликована: Янв. 1, 2024

Electricity load forecasting is important to planning the decision-making regarding use of energy resources, in which power system must be operated guarantee supply electricity future at lowest possible price. With rise ability based on deep learning, these approaches are promising this context. Considering attention mechanism capture long-range dependencies, it highly recommended for sequential data processing, where time series-related tasks stand out. a sequence-to-sequence (Seq2Seq) series Brazil, paper proposes long short-term memory (LSTM) with perform forecasting. The proposed Seq2Seq-LSTM outperforms other well-established models. Having mean absolute error equal 0.3027 method shown field applications. contributes by implementing an Seq2Seq data, therefore, more than one correlated signal can used prediction enhancing its capacity when avaliable.

Язык: Английский

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Enhancing hydroelectric inflow prediction in the Brazilian power system: A comparative analysis of machine learning models and hyperparameter optimization for decision support

Electric Power Systems Research, Год журнала: 2024, Номер 230, С. 110275 - 110275

Опубликована: Март 1, 2024

Язык: Английский

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Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Sensors, Год журнала: 2023, Номер 23(13), С. 6118 - 6118

Опубликована: Июль 3, 2023

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by rise leakage current distribution insulators. This paper presents a novel, hybrid method for fault prediction based on time series contaminated In controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical network were exposed to increasing contamination salt chamber. The was recorded over 28 h effective exposure, culminating flashover all considered event served as mark that this proposes evaluate. proposed applies Christiano-Fitzgerald random walk (CFRW) filter trend decomposition and group data-handling (GMDH) prediction. CFRW filter, with its versatility, proved be more than seasonal using moving averages reducing non-linearities. CFRW-GMDH method, root-mean-squared error 3.44×10-12, outperformed both standard GMDH long short-term memory models superior performance suggested is promising tool predicting faults grid data. approach can provide utilities reliable monitoring insulator health failures, thereby enhancing supply.

Язык: Английский

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The Sustainability Concept: A Review Focusing on Energy

Sustainability, Год журнала: 2023, Номер 15(19), С. 14049 - 14049

Опубликована: Сен. 22, 2023

The concept of sustainability, with a focus on energy, has emerged as central tenet in addressing the mounting global challenges environmental degradation and resource depletion. Indicators sustainability focusing energy are crucial tools used to assess monitor progress toward achieving more sustainable system. These indicators provide valuable insights into environmental, social, economic dimensions practices their long-term impacts. By analyzing understanding these indicators, policymakers, businesses, communities can make informed decisions, formulate effective policies, steer efforts future. serve navigational guides, steering world that support both present needs well-being future generations. In this paper, measurement indexes reviewed, factors. discussion presented here is related an assessment possibilities for improving efficiency evaluating measure whether desired levels being achieved.

Язык: Английский

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