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Energy and AI
Scopus
ISSN 2666-5468 · Elsevier · 738 artikel di indeks
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Artikel Jurnal
Photovoltaic Knowledge-Informed Neural Network (PKINN): Interpretable power prediction model under Fluctuating Environmental Conditions
Fluctuating Environmental Conditions (FECs) are a critical barrier to accurate photovoltaic (PV) power forecasting. Existing models often fail to capture abrupt and stochastic fluctuations, leading to reduced forecasting reliability. To address this challenge, this study proposes an interpretable…
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Artikel Jurnal
Dual-path feature extraction with MoE optimization for ultra-short-term wind power forecasting
Accurate ultra-short-term wind power forecasting is critical for maintaining the stability and efficiency of modern power systems, yet it remains challenging due to the high volatility and nonlinear dynamics of wind energy. This study proposes a hybrid forecasting framework that integrates deep…
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Artikel Jurnal
A transformer-LSTM network enhanced by EEMD for ultra-short-term wind power forecasting
This study aims to improve the dispatch safety and economic efficiency of grid-connected wind power systems by addressing the limitations of traditional ultra-short-term forecasting methods, particularly their inadequate extraction of multi-scale features and limited forecasting accuracy. A…
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Spectrogram-driven unsupervised autoencoder with isolation forest and one-class SVM for lab-scale wind turbine blade fault detection
Wind turbine blades are critical components, and their structural integrity is essential for uninterrupted operation and minimizing downtime. Although various methods are used to monitor the health of wind turbine blades, several research challenges persist, such as the reliance on manual feature…
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Artikel Jurnal
A novel spatiotemporal relation fused network for solar photovoltaic power forecasting
Accurate prediction of solar photovoltaic power is crucial for renewable integration. However, existing methods struggles to simultaneously capture its complex spatial correlation and nonlinear temporal dependencies. Here, we propose a novel spatiotemporal relationship fusion network (STRFN) for…
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Artikel Jurnal
A bi-level advanced control framework for large-scale control of buildings with system-level impact
Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control…
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Towards secure federated learning for energy forecasting under adversarial attacks
Federated learning is increasingly used in energy forecasting, enabling buildings to collaboratively predict load, photovoltaic generation, and prosumption while preserving data privacy. However, this collaborative nature introduces new vulnerabilities, as manipulations by a single participant can…
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A graph neural network enhanced decision transformer for efficient optimization in dynamic smart charging environments
Electric-vehicle smart charging requires quick decision-making under uncertainty while enforcing strict electricity grid and user requirements. Mathematical optimization becomes too slow at scale, while online reinforcement learning struggles with sparse rewards and safety. This paper proposes…
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Artikel Jurnal
Evolving power system operator rules for real-time congestion management
The growing integration of renewable energy sources and the widespread electrification of the energy demand have significantly reduced the capacity margin of the electrical grid. This demands a more flexible approach to grid operation, for instance, combining real-time topology optimization and…
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Practicality-enhanced behind-the-meter PV power generation disaggregation based on synchronization and transferability fused LSTM framework
To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated, the availability of community-level behind-the-meter (BTM) PV power generation is crucial. Yet, due to the scarcity of smart meters installed, it is challenging to obtain such…
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Artikel Jurnal
An analytical solutions-enhanced neural network for high-accuracy modeling: A case study on gas turbine
Physics-informed neural networks have gained wide application due to their data efficiency and generation ability. Nevertheless, their application is restricted in practical engineering problems where complex physical mechanisms make it difficult to formulate or solve the corresponding partial…
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Artikel Jurnal
Graph reinforcement learning for power grids: A comprehensive survey
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to…
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Edge-cloud artificial intelligence digital twin thermal modeling for rotating sintered core heat pipes
The sintered core heat pipe is widely used in precision thermal control and aerospace systems due to its high heat transfer performance. However, conventional computational fluid dynamics approaches for predicting its thermal behavior are computationally expensive and inflexible under varying…
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TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems
Reliable anomaly detection in photovoltaic (PV) inverters is critical for ensuring operational efficiency and reducing maintenance costs in renewable energy systems. We introduce TRACE (Time series Representation learning with Autoencoder-based Contrastive Embeddings), a self-supervised contrastive…
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Artikel Jurnal
Interpretable transformer based intra-day solar forecasting with spatiotemporal satellite and numerical weather prediction inputs
With the increasing capacity addition of solar energy systems, solar forecasting is vital and cost-effective to mitigate solar variability and to support their operation. The temporal fusion transformer (TFT) has shown great potential in both solar irradiance and power output forecasting using…
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Bubble segmentation and heat flux estimation in nucleate boiling using a resource-efficient deep learning framework
Boiling is a critical heat transfer mechanism in high-power-density systems driven by bubble ebullition cycles that efficiently manage thermal loads. However, the simultaneous challenges of the millisecond-scale lifecycle of strongly deformable bubbles and the heavy computational burden of…
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Assessing long-term electricity market design for ambitious decarbonization targets using multi-agent reinforcement learning
Electricity systems are key to transforming today’s society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping the electricity generation mix. In light of the need for more advanced tools to…
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Artikel Jurnal
Thermal conductivity prediction of BN composites based on Enhanced Co-ANN combined with physical attention mechanisms
This study proposes an enhanced co-training artificial neural network (Enhanced Co-ANN) guided by the physical attention mechanisms for the effective thermal conductivity prediction of high filler volume fraction polymer/BN composites. The thermal conductivity of BN composites is influenced by…
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Artikel Jurnal
A World Model framework with digital twin for scheduling demand-side resources under systemic complexity
Modern power grids face profound challenges in scheduling massive, heterogeneous demand-side resources (DSRs), whose collective behaviors often lead to systemic unpredictability and scheduling inaccuracies. Traditional methods, often based on simplified models, struggle to manage this emergent…
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Artikel Jurnal
MI-VMD-BSCNet: A lightweight spatiotemporal modeling framework for tube temperature prediction in coal-fired boiler water-walls
Over-temperature of boiler water-walls causes tube leakage in ultra-supercritical coal-fired power units. This is a critical issue intensified by frequent load fluctuations from flexible peak shaving, essential for carbon peaking and neutrality goals. Existing computational fluid dynamics methods…