Optimal scheduling of renewable energy microgrids: A robust multi-objective approach with machine learning-based probabilistic forecasting

Applied Energy, Год журнала: 2024, Номер 369, С. 123548 - 123548

Опубликована: Июнь 5, 2024

Язык: Английский

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Review of Energy Management Systems in Microgrids

Applied Sciences, Год журнала: 2024, Номер 14(3), С. 1249 - 1249

Опубликована: Фев. 2, 2024

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple generation with different features are used in microgrids, managing these becomes an important problem. The generated power of modules and turbines microgrids is constantly changing irradiation speed. Due to this impermanent uncertain nature renewable resources, generally, storage systems employed microgrid systems. To control the units sustain supply demand balance within provide sustainable reliable loads, management used. Many methods realize optimize microgrids. This review article provides a comparative critical analysis system can be tailored for purposes, which also discussed detail. Additionally, various uncertainty measurement summarized manage variability intermittency sources load demand. Finally, some thoughts about potential future directions practical applications given.

Язык: Английский

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A backpropagation neural network-based hybrid energy recognition and management system

Energy, Год журнала: 2024, Номер 297, С. 131264 - 131264

Опубликована: Апрель 8, 2024

Язык: Английский

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Enhancing interpretability in power management: A time-encoded household energy forecasting using hybrid deep learning model

Energy Conversion and Management, Год журнала: 2024, Номер 315, С. 118795 - 118795

Опубликована: Июль 16, 2024

Nowadays, residential households, including both consumers and emerging prosumers, have exhibited a growing demand for active/reactive power. This surge arises from activities such as charging electrical devices, leveraging flexible resources, integrating renewable energy sources. To meet this escalating effectively, operators must ensure the provision of an ample supply Achieving necessitates identification influential factors generation precise forecasts power demand. Hence, work proposes efficient hybrid deep learning model consisting long short-term memory self-Attention (LSTM-Attention). incorporates explicit time encoding to forecast one-hour-ahead consumption active reactive using real-time data units. The integration models represents strategic development robustness. Leveraging inherent strengths architectures allows synergistic compensation that addresses limitations within each, contributing overall effective forecasting model. Moreover, Shapley Additive Explanations (SHAP) framework was employed interpretability, investigation underscores pivotal role incorporating temporal features into forecasting. SHAP findings can be effectively applied in management strategies optimally enhance response. Finally, evaluate effectiveness proposed model, comprehensive array performance metrics employed. results demonstrate superior accuracy compared alternative models. achieved lowest root mean square error (RMSE), absolute (MAE), percentage (MAPE) with value 0.0256, 0.0181, 14.255 %, respectively. formulated method also significantly contribute industrial sector by improving forecasting, thereby enhancing interpretability identifying most critical factors.

Язык: Английский

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Achieving grid resilience through energy storage and model reference adaptive control for effective active power voltage regulation

Energy Conversion and Management X, Год журнала: 2024, Номер 22, С. 100533 - 100533

Опубликована: Янв. 28, 2024

This article presents a comprehensive examination of the utilization energy storage units for voltage regulation in grids. Specifically, focus is on practical implementation active power control using Model Adaptive Control (MRAC) algorithm. The provides detailed description algorithm, considering grid parameters and showcasing application through MRAC. results implementing an unit global are discussed, highlighting advantages superiority this method.

Язык: Английский

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Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids

Energy Conversion and Economics, Год журнала: 2024, Номер 5(4), С. 259 - 279

Опубликована: Авг. 1, 2024

Abstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been predominant means storage. However, technological advancements led recognition hydrogen as a promising solution address long‐term requirements microgrid systems. This study conducted comprehensive literature review aimed at analysing and synthesizing principal optimization control methodologies employed in hydrogen‐based within context building infrastructures. A comparative assessment was evaluate merits disadvantages different approaches. The techniques for management are categorized based predictability, deployment feasibility, computational complexity. In addition, proposed ranking system facilitates an understanding its suitability diverse applications. encompasses deterministic, stochastic, cutting‐edge methodologies, such machine learning‐based approaches, compares discusses respective merits. key outcome this research is classification various strategy MG, along with mechanism identify which will be suitable under what conditions. Finally, detailed examination advantages strategies controlling optimizing hybrid systems emphasis utilization provided.

Язык: Английский

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Robust deep learning model with attention framework for spatiotemporal forecasting of solar and wind energy production

Energy Conversion and Management X, Год журнала: 2025, Номер unknown, С. 100919 - 100919

Опубликована: Фев. 1, 2025

Язык: Английский

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Enhancing Weather Forecasting Integrating LSTM and GA

Applied Sciences, Год журнала: 2024, Номер 14(13), С. 5769 - 5769

Опубликована: Июль 1, 2024

Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression deep learning methods for forecasting to deal with this variability. This research employs models estimate missing historical data three different time horizons, incorporating long short-term memory (LSTM) forecast short- medium-term conditions at Quinta de Santa Bárbara in the Douro region. Additionally, a genetic algorithm (GA) is used optimize LSTM hyperparameters. The results obtained show proposed optimized effectively reduced evaluation metrics across horizons. underscore importance of accurate making important decisions various sectors.

Язык: Английский

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Statistical evaluation of a diversified surface solar irradiation data repository and forecasting using a recurrent neural network-hybrid model: A case study in Bhutan

Renewable Energy, Год журнала: 2025, Номер unknown, С. 122706 - 122706

Опубликована: Март 1, 2025

Язык: Английский

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Multistep Probabilistic Forecasting Approach for Tunnel Boring Machine Cutterhead Torque and Thrust Based on VMD-BDNN

International Journal of Geomechanics, Год журнала: 2025, Номер 25(7)

Опубликована: Апрель 16, 2025

Язык: Английский

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