Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm

Results in Engineering, Journal Year: 2023, Volume and Issue: 17, P. 100892 - 100892

Published: Jan. 13, 2023

Conical picks are widely used as cutting tools in shearers and roadheaders, the mean force (MCF) is one of important parameters affecting conical pick performance. As MCF depends on a number due to that existing empirical theoretical formulas numerical modelling not sufficient enough reliable predict proficient manner. So, this research, novel intelligent model based random forest algorithm (RF) heuristic called salp swarm (SSA) have been applied determine optimal hyper-parameters RF, root square error fitness function. A total 188 data samples including 50 rock types seven (tensile strength σt, compressive σc, cone angle θ, depth d, attack γ, rake α back-clearance β) were collected develop an SSA-RF for prediction. The prediction results compared with influential four classical models, such forest, extreme learning machine, support vector machine radial basis function neural network. absolute (MAE), (RMSE), percentage (MAPE) Pearson correlation coefficient (R2) employed evaluation indexes compare capability different predicting models. MAE (0.509 0.996), RMSE (0.882 1.165), MAPE (0.146 0.402) R2 (0.975 0.910) values between measured predicted training testing phases clearly demonstrate superiority other tools. sensitivity analysis has also performed understand influence each input parameter MCF, which indicates d σt most variables

Language: Английский

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Power grid based renewable energy analysis by photovoltaic cell machine learning architecture in wind energy hybridization

Sustainable Energy Technologies and Assessments, Journal Year: 2023, Volume and Issue: 57, P. 103243 - 103243

Published: May 15, 2023

Language: Английский

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A flexible and lightweight deep learning weather forecasting model

Applied Intelligence, Journal Year: 2023, Volume and Issue: 53(21), P. 24991 - 25002

Published: Aug. 1, 2023

Abstract Numerical weather prediction is an established forecasting technique in which equations describing wind, temperature, pressure and humidity are solved using the current atmospheric state as input. This study examines deep learning to forecast given historical data from two London-based locations. Two distinct Bi-LSTM recurrent neural network models were developed TensorFlow framework trained make predictions next 24 72 h, past 120 h. The first predicted temperature at Kew Gardens with a accuracy of $$\pm$$ ± 2 $${}^{\circ }$$ ∘ C 73% instances whole unseen year, root mean squared errors 1.45 C. second 72-h air relative Heathrow 2.26 14% respectively 80% within 3 while 20%. Both networks five years data, cloud training times over minute (24-h network) three minutes (72-h).

Language: Английский

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Data Analysis in Wind Power Prediction: An Essential Step Before Data-Based Modeling

Published: Jan. 1, 2025

Language: Английский

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Data-driven machinery fault diagnosis: A comprehensive review

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129588 - 129588

Published: Feb. 1, 2025

Language: Английский

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Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101531 - 101531

Published: Feb. 1, 2025

Language: Английский

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Predictive Wind Turbine Power Analysis Based on SCADA Data and Machine Learning Algorithms

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 32 - 42

Published: Jan. 1, 2025

Language: Английский

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XGBoost (Aşırı Gradyan Artırımlı Karar Ağaçları) ile Hidroelektrik Enerji Tahmini

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, Journal Year: 2025, Volume and Issue: 40(1), P. 205 - 218

Published: March 26, 2025

Hidroelektrik enerji, Türkiye'nin hızlı ekonomik ve nüfus artışıyla artan enerji talebinin karşılanmasında büyük önem taşır. Mevsimsel bağımlılığı nedeniyle hidroelektrik tahmin algoritmaları için uygundur. Bu çalışma, Türkiye'de 100 MW'ın üzerinde güç üreten EÜAŞ Aslantaş HES'de üretimini etmeyi amaçlamaktadır. Tahmin modeli, XGBoost (Aşırı Gradyan Artırımlı karar ağaçları) ile tarih-saat kayıtları, geçmiş üretim verileri sıcaklık gibi çeşitli girdi kullanılarak oluşturulmuştur. Üretim verileri, EPİAŞ Şeffaflık Platformu’ndan alınmış Python işlenmiştir. farklı ağaç sayıları öğrenme oranı (η) denenerek optimize edilmiştir. Modelin etkinliği, belirleme katsayısı (R²), Ortalama Mutlak Ölçekli Hata (MASE), Kök Karesel (RMSE), (MAE) Ağırlıklı Yüzdesel (WAPE) hata ölçümleri titizlikle değerlendirilmiştir. çalışmada kullanılan yöntemler elde edilen sonuçlar, tahmininde makine öğrenimi algoritmalarının faydalı olabileceğini yönetimi stratejilerinin edilmesine yönelik önemli bilgiler sunabileceğini göstermektedir.

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A taxonomy of key management schemes of SCADA systems

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 124, P. 110366 - 110366

Published: April 25, 2025

Language: Английский

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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Journal of Artificial Intelligence and Soft Computing Research, Journal Year: 2023, Volume and Issue: 13(3), P. 197 - 210

Published: June 1, 2023

Abstract In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with use of various types Deep Neural Networks (DNNs). The impact prediction time horizon length on accuracy, and influence temperature effectiveness have been analyzed. Three DNNs implemented tested, including: CNN (Convolutional Networks), GRU (Gated Recurrent Unit), H-MLP (Hierarchical Multilayer Perceptron). DNN architectures are part Learning (DLP) framework that applied in System (DLPPS). system trained based data comes from a real wind farm. This significant because results strongly depend weather conditions specific locations. obtained proposed system, for data, presented compared. best result has achieved network. key advantage high using minimal subset parameters. power farms very important as capacity shown rapid increase, become promising source renewable energies.

Language: Английский

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