Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field

BIO Web of Conferences, Journal Year: 2025, Volume and Issue: 157, P. 07002 - 07002

Published: Jan. 1, 2025

Expanding exploration activities into new fields has significantly boosted oil production. Well logging is a key method in petroleum exploration, used to evaluate hydrocarbon zones by analyzing parameters such as gamma ray, porosity, density, resistivity, and wave propagation velocity. These are displayed vertical log curves against well depth. However, tools sometimes fail capture formation accurately, creating gaps data. Sonic data particularly prone gaps, they newer less common older wells. To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. Gradient boosting employs ensemble of decision trees, iteratively correcting errors model complex patterns. This especially suitable for handling the intricate nature In this study, Python was develop predictions demonstrating capability enhance reliability improve processes. By bridging ensures more accurate assessments zones, supporting better outcomes.

Language: Английский

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The Role of Utilizing Artificial Intelligence and Renewable Energy in Reaching Sustainable Development Goals

Renewable Energy, Journal Year: 2024, Volume and Issue: 235, P. 121311 - 121311

Published: Sept. 7, 2024

Language: Английский

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A multi-energy meta-model strategy for multi-step ahead energy load forecasting

Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Language: Английский

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Efficiency in Building Energy Use: Pattern Discovery and Crisis Identification in Hot-Water Consumption data

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115579 - 115579

Published: March 1, 2025

Language: Английский

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Optimizing deep neural network architectures for renewable energy forecasting

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: Nov. 12, 2024

An accurate renewable energy output forecast is essential for efficiency and power system stability. Long Short-Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Gated Recurrent Unit(GRU), Convolutional Neural Network-LSTM(CNN-LSTM) Deep Network (DNN) topologies are tested solar wind production forecasting in this study. ARIMA was compared to the models. This study offers a unique architecture Networks (DNNs) that specifically tailored forecasting, optimizing accuracy by advanced hyperparameter tuning incorporation of meteorological temporal variables. The optimized LSTM model outperformed others, with MAE (0.08765), MSE (0.00876), RMSE (0.09363), MAPE (3.8765), R2 (0.99234) values. GRU, CNN-LSTM, BiLSTM models predicted well. Meteorological time-based factors enhanced accuracy. addition sun data improved its prediction. results show deep neural network can predict energy, highlighting importance carefully selecting characteristics fine-tuning model. work improves estimates promote more reliable environmentally sustainable electricity system.

Language: Английский

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Modeling the Efficiency of Resource Consumption Management in Construction Under Sustainability Policy: Enriching the DSEM-ARIMA Model

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 10945 - 10945

Published: Dec. 13, 2024

The aim of this research is to study the influence factors affecting efficiency resource consumption under sustainability policy based on using DSEM-ARIMA (Dyadic Structural Equation Modeling Autoregressive Integrated Moving Average) model. performed Thailand experience. findings indicate that continuous economic growth aligns with country’s objectives, directly contributing social growth. This efficient planning. It demonstrates management goal achieving 5.0. Furthermore, considering environmental aspect, it found and impacts ecological aspect due significant in construction. construction shows a rate increase 264.59% (2043/2024), reaching 401.05 ktoe (2043), which exceeds carrying capacity limit set at 250.25 ktoe, resulting long-term degradation. Additionally, political have greatest environment, exacerbating damage beyond current levels. Therefore, model establishes new scenario policy, indicating leads degradation reduced 215.45 does not exceed capacity. Thus, if utilized, can serve as vital tool formulating policies steer toward 5.0 effectively.

Language: Английский

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SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

Electronics, Journal Year: 2024, Volume and Issue: 13(11), P. 2071 - 2071

Published: May 27, 2024

We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This uses self-attention-based temporal convolutional network (TCN) process and predict PV outputs with high precision. perform meticulous data preprocessing ensure accurate normalization outlier rectification, which are vital for reliable analysis. The TCN layers crucial capturing patterns energy data; we complement them the teacher forcing technique during training phase significantly enhance sequence prediction accuracy. By optimizing hyperparameters Optuna, further improve model’s performance. Our incorporates multi-head self-attention mechanisms focus on most impactful features, thereby improving In validations against datasets from nine regions Korea, outperformed conventional methods. results indicate that is robust tool systems’ management operational efficiency can contribute Korea’s pursuit of sustainable solutions.

Language: Английский

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Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous Federated smart grids

Internet of Things, Journal Year: 2024, Volume and Issue: unknown, P. 101376 - 101376

Published: Sept. 1, 2024

Language: Английский

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Short-Term Power Load Forecasting in City Based on ISSA-BiTCN-LSTM

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 10, 2025

Language: Английский

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Geospatial Forecasting of Electric Energy in Distribution Systems Using Segmentation and Machine Learning with Convolutional Methods

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 424 - 424

Published: Jan. 19, 2025

This paper proposes an innovative methodology for geospatial forecasting of electrical demand across various consumption segments and scales, integrating machine learning discrete convolution within the framework global system projections. The study was conducted in two phases: first, techniques were utilized to classify determine relative growth with similar patterns. In second phase, methods employed produce accurate spatial forecasts by incorporating influence neighboring areas through a “core matrix” accounting geographical constraints regions without consumption. proposed approach enhances precision forecasts, making it suitable large-scale distribution systems implementable short timeframes. method validated using data from Peruvian serving over one million users, employing 204 historical records analyzing three georeferenced at scales 1:10,000, 1:1000, 1:100. results demonstrate its effectiveness different time horizons, thereby contributing improved planning infrastructure.

Language: Английский

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