CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability

Energy, Год журнала: 2024, Номер 293, С. 130666 - 130666

Опубликована: Фев. 10, 2024

Язык: Английский

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TDCN: A novel temporal depthwise convolutional network for short-term load forecasting

International Journal of Electrical Power & Energy Systems, Год журнала: 2025, Номер 165, С. 110512 - 110512

Опубликована: Фев. 5, 2025

Язык: Английский

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Energy optimization management of microgrid using improved soft actor-critic algorithm

International Journal of Renewable Energy Development, Год журнала: 2024, Номер 13(2), С. 329 - 339

Опубликована: Фев. 20, 2024

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well complexities of solving high-dimensional energy management mathematical models mi-crogrid optimization, a microgrid optimization method is proposed based on an improved soft actor-critic algorithm. In method, algorithm employs entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities any part action space, which can simplify analysis process generation while effectively mitigating convergence fragility issues model management. The effectiveness validated through case study op-timization results revealed increase 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, 36.33% total profits compared Deep Q-network algorithm, state-action-reward-state-action proximal policy ant-colony strategy genetic fuzzy inference system, theoretical retailer stragety, respectively. Additionally, com-pared other methods strategies, learn more optimal behaviors anticipate fluctuations electricity prices demand.

Язык: Английский

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Graph Convolutional Networks based short-term load forecasting: Leveraging spatial information for improved accuracy

Electric Power Systems Research, Год журнала: 2024, Номер 230, С. 110263 - 110263

Опубликована: Март 5, 2024

Язык: Английский

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Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load

Energy, Год журнала: 2024, Номер 302, С. 131814 - 131814

Опубликована: Май 26, 2024

Язык: Английский

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Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and Hybrid Kernel Extreme Learning Machine

Entropy, Год журнала: 2024, Номер 26(8), С. 699 - 699

Опубликована: Авг. 17, 2024

To meet the challenges of energy sustainability, integrated system (IES) has become a key component in promoting development innovative systems. Accurate and reliable multivariate load prediction is prerequisite for IES optimal scheduling steady running, but uncertainty fluctuation many influencing factors increase difficulty forecasting. Therefore, this article puts forward multi-energy approach IES, which combines fennec fox optimization algorithm (FFA) hybrid kernel extreme learning machine. Firstly, comprehensive weight method used to combine entropy Pearson correlation coefficient, fully considering information content correlation, selecting affecting prediction, ensuring that input features can effectively modify results. Secondly, coupling relationship between learned predicted using At same time, FFA parameter optimization, reduces randomness setting. Finally, utilized measured data at Arizona State University verify its effectiveness The results indicate mean absolute error (MAE) proposed 0.0959, 0.3103 0.0443, respectively. root square (RMSE) 0.1378, 0.3848 0.0578, weighted percentage (WMAPE) only 1.915%. Compared other models, model higher accuracy, with maximum reductions on MAE, RMSE WMAPE 0.3833, 0.491 2.8138%,

Язык: Английский

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Deep convolutional neural networks for short-term multi-energy demand prediction of integrated energy systems

International Journal of Electrical Power & Energy Systems, Год журнала: 2024, Номер 160, С. 110111 - 110111

Опубликована: Июль 10, 2024

Язык: Английский

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The balance issue of the proportion between new energy and traditional thermal power: An important issue under today's low-carbon goal in developing countries

Renewable Energy, Год журнала: 2024, Номер 231, С. 121018 - 121018

Опубликована: Июль 19, 2024

Язык: Английский

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An intelligent model for efficient load forecasting and sustainable energy management in sustainable microgrids

Discover Sustainability, Год журнала: 2024, Номер 5(1)

Опубликована: Июль 30, 2024

Abstract Microgrids have emerged as a promising solution for enhancing energy sustainability and resilience in localized distribution systems. Efficient management accurate load forecasting are one of the critical aspects improving operation microgrids. Various approaches prediction using statistical models discussed literature. In this work, novel framework that incorporates machine learning (ML) techniques is presented an solar wind generation. The anticipated approach also emphasizes time series-based microgrids with precise estimation State Charge (SoC) battery. A unique feature proposed utilizes historical data employs series analysis coupled different ML to forecast demand commercial scenario. Long Short-Term Memory (LSTM) Linear Regression (LR) employed experimental study under three cases, such (i) generation, (ii) and, (iii) SoC results show Random Forest (RF) LSTM performs well respectively. On other hand, Artificial Neural Network (ANN) model exhibited superior accuracy terms estimation. Further, Graphical User Interface (GUI) developed evaluating efficacy framework.

Язык: Английский

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Pattern Shared Vision Refinement for Enhancing Collaboration and Decision-Making in Government Software Projects

Electronics, Год журнала: 2025, Номер 14(2), С. 334 - 334

Опубликована: Янв. 16, 2025

This study proposes a new approach and explores how pattern recognition enhances collaboration between users Agile teams in software development, focusing on shared resources decision-making efficiency. Using domain-specific modeling languages (DSMLs) within security-by-design framework, the research identifies patterns that support team selection, effort estimation, risk management for Afghanistan’s ministries. These align development with governmental needs by clarifying stakeholder roles fostering cooperation. The builds p-mart-Repository-Programs (P-MARt) repository, integrating data mining, algorithms, ETL (Extract, Transform, Load) processes to develop innovative methodologies. approaches enable dynamic knowledge management, refine documentation, improve project outcomes. Central this is our Pattern Shared Vision Refinement (PSVR) approach, which emphasizes robust collaboration, security, adaptability. By addressing challenges unique operations, PSVR strengthens practices ensures high-quality delivery. analyzing historical trends introducing strategies, underscores critical role of aligning organizational goals. It demonstrates systematic identification can optimize interaction secure consensus, ultimately enhancing engineering outcomes context.

Язык: Английский

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