Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model DOI Creative Commons

Jiawen Ye,

Lei Dai,

HaiYing Wang

et al.

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(10), P. 26916 - 26950

Published: Jan. 1, 2024

<p>Accurate prediction of sewage flow is crucial for optimizing treatment processes, cutting down energy consumption, and reducing pollution incidents. Current models, including traditional statistical models machine learning have limited performance when handling nonlinear high-noise data. Although deep excel in time series prediction, they still face challenges such as computational complexity, overfitting, poor practical applications. Accordingly, this study proposed a combined model based on an improved sparrow search algorithm (SSA), convolutional neural network (CNN), transformer, bidirectional long short-term memory (BiLSTM) prediction. Specifically, the CNN part was responsible extracting local features from series, Transformer captured global dependencies using attention mechanism, BiLSTM performed temporal processing features. The SSA optimized model's hyperparameters to improve accuracy generalization capability. validated dataset actual plant. Experimental results showed that introduced mechanism significantly enhanced ability handle data, effectively hyperparameter selection, improving training efficiency. After introducing SSA, CNN, modules, $ {R^{\text{2}}} increased by 0.18744, RMSE (root mean square error) decreased 114.93, MAE (mean absolute 86.67. difference between predicted peak/trough monitored within 3.6% appearance 2.5 minutes away time. By employing multi-model fusion approach, achieved efficient accurate highlighting potential application prospects field treatment.</p>

Language: Английский

Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis DOI Creative Commons
Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100903 - 100903

Published: Jan. 1, 2025

Language: Английский

Citations

4
Hybrid prediction method for solar photovoltaic power generation using normal cloud parrot optimization algorithm integrated with extreme learning machine DOI Creative Commons
Huachen Liu, Changlong Cai,

Pangyue Li

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 22, 2025

As the energy crisis environmental concerns rise, harnessing renewable sources like photovoltaics (PV) is critical for sustainable development. However, seasonal variability and random intermittency of solar power pose significant forecasting challenges, threatening grid stability. Therefore, this paper proposes a novel hybrid method, NCPO-ELM, to adequately capture spatial temporal dependencies within meteorological data crucial accurate predictions. To effectively optimize performance Extreme Learning Machine (ELM), Normal Cloud Parrot Optimization (NCPO) algorithm developed, inspired by Pyrrhura Molinae parrots' flock behavior cloud model theory. NCPO integrates five unique search strategies utilizes structure explore exploit. By introducing normal generate samples with specific distributions, enhances solution space coverage. subsequently employed Single-Layer Feedforward Network (SLFN) hidden layer hyperparameters, yielding optimal weights biases output layer, thereby reducing benchmark ELM's sensitivity noise instability from initialization. The actual results PV stations across different regions demonstrate that proposed NCPO-ELM shows superior prediction accuracy compared existing approaches, particularly time series diverse characteristics variations.

Language: Английский

Citations

1
Forecasting solar power generation using evolutionary mating algorithm-deep neural networks DOI Creative Commons
Mohd Herwan Sulaiman, Zuriani Mustaffa

Energy and AI, Journal Year: 2024, Volume and Issue: 16, P. 100371 - 100371

Published: April 17, 2024

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases deep neural networks (DNN) for forecasting solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC output using real plant measurements spanning 34-day period, recorded at 15-minute intervals. intricate nonlinear relationship between irradiation, ambient temperature, module temperature is captured accurate prediction. Additionally, conducts comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search (HSA-DNN), DNN Adaptive Moment Estimation optimizer (ADAM) Nonlinear AutoRegressive eXogenous inputs (NARX). experimental results distinctly highlight exceptional performance EMA-DNN by attaining lowest Root Mean Squared Error (RMSE) during testing. contribution not only advances methodologies but also underscores potential merging algorithms contemporary improved accuracy reliability.

Language: Английский

Citations

6
Method and Validation of Coal Mine Gas Concentration Prediction by Integrating PSO Algorithm and LSTM Network DOI Open Access
Guangyu Yang, Quanjie Zhu,

Dacang Wang

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(5), P. 898 - 898

Published: April 28, 2024

Gas concentration monitoring is an effective method for predicting gas disasters in mines. In response to the shortcomings of low efficiency and accuracy conventional prediction, a new prediction based on Particle Swarm Optimization Long Short-Term Memory Network (PSO-LSTM) proposed. First, principle PSO-LSTM fusion model analyzed, analysis constructed. Second, data are normalized preprocessed. The PSO algorithm utilized optimize training set LSTM model, facilitating selection model. Finally, MAE, RMSE, coefficient determination R2 evaluation indicators proposed verify analyze results. comparison verification research was conducted using measured mine as sample data. experimental results show that: (1) maximum RMSE predicted 0.0029, minimum 0.0010 when size changes. This verifies reliability effect (2) predictive performance all models ranks follows: > SVR-LSTM PSO-GRU. Comparative with demonstrates that more concentration, further confirming superiority this prediction.

Language: Английский

Citations

6
An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction DOI

Jie Chen,

Peng Tian,

Shijie Qian

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124738 - 124738

Published: Oct. 22, 2024

Language: Английский

Citations

6
Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data DOI Creative Commons

Hong Wu,

Haipeng Liu, Huaiping Jin

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(18), P. 4739 - 4739

Published: Sept. 23, 2024

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning operation of existing systems. To enhance accuracy PV prediction ensure safe system, novel approach based on seasonal division periodic attention mechanism (PAM) for proposed. First, dataset divided into three components trend, period, residual under fuzzy c-means clustering (FCM) decomposition (SD) method according four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed these subsequences. Then, network optimized using improved Newton–Raphson genetic algorithm (NRGA), innovative PAM added focus characteristics data. Finally, results each component summarized obtain final results. A case study Australian DKASC Alice Spring plant demonstrates performance proposed approach. Compared with other paper models, MAE, RMSE, MAPE evaluation indexes show that has excellent in predicting output stability.

Language: Английский

Citations

4
Systematic literature review on Industry 5.0: current status and future research directions with insights for the Asia Pacific countries DOI Creative Commons
Imran Ali, Khoa A. Nguyen, Ingyu Oh

et al.

Asia Pacific Business Review, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 28

Published: Feb. 4, 2025

Language: Английский

Citations

0
SMART SYSTEM UNTUK PEMANTAUAN DAN OPTIMASI KINERJA PEMBANGKIT LISTRIK TENAGA SURYA DOI Open Access

Erlan Taneza,

Firdaus Firdaus

Transmisi Jurnal Ilmiah Teknik Elektro, Journal Year: 2025, Volume and Issue: 1(1), P. 20 - 32

Published: Jan. 31, 2025

Pembangkit Listrik Tenaga Surya (PLTS) menjadi salah satu solusi utama dalam pemanfaatan energi terbarukan. Namun, tantangan hal pemantauan, perawatan, dan optimasi kinerja PLTS masih isu yang perlu diatasi. Artikel ini mengeksplorasi peran smart system berbasis Internet of Things (IoT), kecerdasan buatan (AI), machine learning (ML) meningkatkan efisiensi operasional PLTS. Melalui systematic literature review, penelitian mengidentifikasi berbagai metode teknologi telah digunakan untuk deteksi, prediksi, Hasil menunjukkan bahwa mampu pemantauan hingga 95%, prediksi kerusakan 110,8%, output 130% dibanding dengan pendekatan manual-konvensional. Penggunaan memberikan efektif menghadapi jangka panjangnya. Temuan menawarkan panduan praktis rekomendasi pengembangan lebih lanjut pengelolaan cerdas.

Citations

0
SMART SYSTEM UNTUK PEMANTAUAN DAN OPTIMASI KINERJA PEMBANGKIT LISTRIK TENAGA SURYA DOI Open Access

Erlan Taneza,

Firdaus Firdaus

Transmisi Jurnal Ilmiah Teknik Elektro, Journal Year: 2025, Volume and Issue: 27(1), P. 20 - 32

Published: Jan. 31, 2025

Pembangkit Listrik Tenaga Surya (PLTS) menjadi salah satu solusi utama dalam pemanfaatan energi terbarukan. Namun, tantangan hal pemantauan, perawatan, dan optimasi kinerja PLTS masih isu yang perlu diatasi. Artikel ini mengeksplorasi peran smart system berbasis Internet of Things (IoT), kecerdasan buatan (AI), machine learning (ML) meningkatkan efisiensi operasional PLTS. Melalui systematic literature review, penelitian mengidentifikasi berbagai metode teknologi telah digunakan untuk deteksi, prediksi, Hasil menunjukkan bahwa mampu pemantauan hingga 95%, prediksi kerusakan 110,8%, output 130% dibanding dengan pendekatan manual-konvensional. Penggunaan memberikan efektif menghadapi jangka panjangnya. Temuan menawarkan panduan praktis rekomendasi pengembangan lebih lanjut pengelolaan cerdas.

Citations

0
A critical review of technical case studies for electricity theft detection in smart grids: A new paradigm based transformative approach DOI Creative Commons
Muhammad Sajid Iqbal,

Shoaib Munawar,

Muhammad Gufran Khan

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100965 - 100965

Published: March 1, 2025

Language: Английский

Citations

0