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

Entropy, Journal Year: 2024, Volume and Issue: 26(8), P. 699 - 699

Published: Aug. 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%,

Language: Английский

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A novel prediction model of grounding resistance based on long short-term memory

AIP Advances, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 1, 2025

This study aims to investigate the use of Long Short-Term Memory (LSTM) models for predicting temporal variations in grounding resistance using time series data. analysis is first apply LSTM prediction, utilizing experimental data, including soil resistivity and rainfall. The model trained, validated, tested with various parameters, enabling a comparative assessment its accuracy capturing variations. Furthermore, benchmarks model’s performance against traditional Artificial Neural Networks, confirming LSTM’s superior predictive regarding time-dependent changes resistance. results prediction show that significantly surpasses methods terms mean absolute percentage error, an improvement 72.73% across metrics.

Language: Английский

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ANALISIS PREDIKSI RADIASI MATAHARI DENGAN ALGORITMA MACHINE LEARNING DAN IMPLEMENTASI BAYESIAN OPTIMIZATION DI PROVINSI DKI JAKARTA

Jurnal Manajemen Informatika dan Sistem Informasi, Journal Year: 2025, Volume and Issue: 8(1), P. 45 - 58

Published: Jan. 16, 2025

Peningkatan populasi menyebabkan peningkatan permintaan energi. Hingga saat ini, masalah terkait energi adalah sumber daya yang terbatas. Energi alternatif terbarukan dapat dimanfaatkan secara optimal di masa depan. Salah satu matahari karena jumlahnya melebihi kebutuhan ini dan Hal sejalan dengan target 7.2 dalam Sustainable Development Goals (SDGs) 2030, yaitu meningkatkan porsi signifikan bauran global. Indonesia memiliki potensi melalui radiasi matahari. Namun, pemanfaatan surya sebagai pembangkit listrik Provinsi DKI Jakarta belum optimal. Penelitian bertujuan untuk memprediksi nilai Global Horizontal Irradiance (GHI) harian menggunakan Support Vector Regression (SVR) Bayesian Optimization membandingkannya XGBoost menemukan model terbaik dari hasil prediksi. Metode BO-SVR terbukti memberikan prediksi baik kuat pada data digunakan MAPE RMSE pengujian masing-masing 0,182 34,412. Penerapan menentukan hiperparameter membentuk telah kinerja model. menghasilkan informasi bagi pemerintah, khususnya PT Perusahaan Listrik Negara (PLN) peneliti karakteristik

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HWDQT: A hybrid quantum machine learning method for ultra-short-term distributed photovoltaic power prediction

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110122 - 110122

Published: Feb. 12, 2025

Language: Английский

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A hybrid intelligent time-series framework for predicting short-term LNG sendout rate

Journal of Pipeline Science and Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100268 - 100268

Published: March 1, 2025

Language: Английский

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Structural condition evaluation using unsupervised Bayesian optimized BiLSTM networks

Journal of Civil Structural Health Monitoring, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Language: Английский

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Rapid Prediction Models for Oil Temperature and Surrounding Environment Temperature Fields in Buried Hot Crude Oil Pipelines

Journal of Pipeline Systems Engineering and Practice, Journal Year: 2025, Volume and Issue: 16(3)

Published: May 12, 2025

Language: Английский

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Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: data generation, incremental learning, transfer learning, and physics-informed

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133640 - 133640

Published: Oct. 1, 2024

Language: Английский

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Probabilistic Dual-Adaptive Spatio-Temporal Graph Convolutional Networks for forecasting energy consumption dynamics of electric vehicle charging stations

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 122, P. 109976 - 109976

Published: Dec. 11, 2024

Language: Английский

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Ultra-short-term Single-step Photovoltaic Power Prediction based on VMD-Attention-BiLSTM Combined Model

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Research on photovoltaic systems (PV) power prediction contributes to optimizing configurations, responding promptly emergencies, reducing costs, and maintaining long-term system stability. This study proposes a VMD-Attention-BiLSTM model for predicting ultra-short-term further enhance performance. Firstly, VMD decomposes historical data into multiple sub-sequences with different frequencies, treating each sub-sequence as separate input variable expansion. Secondly, the Attention mechanism calculates correlation coefficients between variables assigns corresponding weights based magnitude of output variable. Finally, BiLSTM adopts dual-layer LSTM structure more accurately extract features. Experimental results show that compared various advanced deep learning methods, MAE combined improves by at least 29%.

Language: Английский

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