Machine Learning Applications in Building Energy Systems: Review and Prospects

Buildings, Год журнала: 2025, Номер 15(4), С. 648 - 648

Опубликована: Фев. 19, 2025

Building energy systems (BESs) are essential for modern infrastructure but face significant challenges in equipment diagnosis, consumption prediction, and operational control. The complexity of BESs, coupled with the increasing integration renewable sources, presents difficulties fault detection, accurate forecasting, dynamic system optimisation. Traditional control strategies struggle low efficiency, slow response times, limited adaptability, making it difficult to ensure reliable operation optimal management. To address these issues, researchers have increasingly turned machine learning (ML) techniques, which offer promising solutions improving scheduling, real-time BESs. This review provides a comprehensive analysis ML techniques applied According results literature review, supervised methods, such as support vector machines random forest, demonstrate high classification accuracy detection require extensive labelled datasets. Unsupervised approaches, including principal component clustering algorithms, robust identification capabilities without data may complex nonlinear patterns. Deep particularly convolutional neural networks long short-term memory models, exhibit superior forecasting Reinforcement further enhances management by dynamically adjusting parameters maximise efficiency cost savings. Despite advancements, remain terms availability, computational costs, model interpretability. Future research should focus on hybrid integrating explainable AI enhancing adaptability evolving demands. also highlights transformative potential BESs outlines future directions sustainable intelligent building

Язык: Английский

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Deep-Learning-Based Scheduling Optimization of Wind-Hydrogen-Energy Storage System on Energy Islands

Energy, Год журнала: 2025, Номер unknown, С. 135107 - 135107

Опубликована: Фев. 1, 2025

Язык: Английский

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Optimal planning for integrated electricity and heat systems using CNN-BiLSTM-attention network forecasts

Energy, Год журнала: 2024, Номер 309, С. 133042 - 133042

Опубликована: Авг. 31, 2024

Язык: Английский

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Capturing complex electricity load patterns: A hybrid deep learning approach with proposed external-convolution attention

Energy Strategy Reviews, Год журнала: 2025, Номер 57, С. 101638 - 101638

Опубликована: Янв. 1, 2025

Язык: Английский

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Using Crafted Features and Polar Bear Optimization Algorithm for Short-Term Electric Load Forecast System

Energy and AI, Год журнала: 2025, Номер unknown, С. 100470 - 100470

Опубликована: Янв. 1, 2025

Язык: Английский

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Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors

Sustainability, Год журнала: 2025, Номер 17(3), С. 1033 - 1033

Опубликована: Янв. 27, 2025

With the proliferation of distributed energy resources, advanced metering infrastructure, and communication technologies, grid is transforming into a flexible, intelligent, collaborative system. Short-term electric load forecasting for individual residential customers playing an increasingly important role in operation planning future grid. Predicting electrical households more challenging with higher uncertainty volatility at household level compared to total feeder regional levels. The previous research results show that accuracy using machine learning single deep model far from adequate there still room improvement.

Язык: Английский

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Short- and medium-term power load forecasting model based on a hybrid attention mechanism in the time and frequency domains

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127329 - 127329

Опубликована: Март 1, 2025

Язык: Английский

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Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

Polymers, Год журнала: 2024, Номер 16(18), С. 2607 - 2607

Опубликована: Сен. 14, 2024

This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type recurrent neural network (RNN), in field polymeric sciences. LSTM networks have shown notable effectiveness modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures dynamic processes polymers. delves into use models polymer properties, monitoring polymerization processes, evaluating degradation mechanical performance Additionally, it addresses challenges related to availability interpretability. Through various case studies comparative analyses, demonstrates different science applications. Future directions also discussed, with an emphasis on real-time applications need interdisciplinary collaboration. The goal this is connect advanced machine learning (ML) techniques science, thereby promoting innovation improving predictive capabilities field.

Язык: Английский

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Multimodal Fusion of Optimized GRU–LSTM with Self-Attention Layer for Hydrological Time Series Forecasting

Water Resources Management, Год журнала: 2024, Номер 38(15), С. 6045 - 6062

Опубликована: Авг. 17, 2024

Язык: Английский

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Energy Consumption Prediction of Cold Storage Based on LSTM with Parameter Optimization

International Journal of Refrigeration, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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