LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting

Journal of Organizational and End User Computing, Journal Year: 2025, Volume and Issue: 37(1), P. 1 - 29

Published: Feb. 21, 2025

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional demand forecasting methods often struggle with complex data processing low prediction accuracy. To address these issues, this paper introduces novel approach that combines deep learning techniques decision support systems. The model integrates advanced techniques, including LSTM Transformer, PSO algorithm parameter optimization, significantly enhancing predictive performance practical applicability. Results show our achieves substantial improvements across various metrics, 30% reduction in MAE, 20% decrease MAPE, 25% drop RMSE, 35% decline MSE. These results validate model's effectiveness reliability forecasting. This research provides valuable insights applying

Language: Английский

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Accurate solar power prediction with advanced hybrid deep learning approach

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110367 - 110367

Published: March 1, 2025

Language: Английский

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Prediction of Crystalline Structure Evolution During Solidification of Aluminum at Different Cooling Rates Using a Hybrid Neural Network Model

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104578 - 104578

Published: March 1, 2025

Language: Английский

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PV Generation Prediction Using Multilayer Perceptron and Data Clustering for Energy Management Support

Energies, Journal Year: 2025, Volume and Issue: 18(6), P. 1378 - 1378

Published: March 11, 2025

Accurate PV power generation forecasting is critical to enable grid utilities manage energy effectively. This study presents an approach that combines machine learning with a clustering methodology improve the accuracy of predictions for management purposes. First, various models were compared, and multilayer perceptron (MLP) outperformed others by effectively capturing complex relationships between weather parameters output, obtaining following results: MSE: 3.069, RMSE: 1.752, MAE: 1.139. To performance MLP, characteristics are highly correlated outputs, such as irradiation sun elevation, grouped using K-means clustering. The elbow method identified four optimal clusters, individual MLP trained on each, reducing data complexity improving model focus. clustering-based significantly improved predictions, resulting in average metrics across all clusters following: 0.761, 0.756, 0.64. Despite these improvements, further research optimizing architecture required address inconsistencies achieve even better performance.

Language: Английский

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Robust Clustering and Anomaly Detection of User Electricity Consumption Behavior Based on Correntropy

IET Generation Transmission & Distribution, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT Anomaly detection in power systems is crucial for ensuring the safety and stability of electrical grids. Traditional methods struggle to extract meaningful features from electricity consumption data due significant differences usage patterns across various user types, such as residential industrial users. Applying a single model all categories increases feature complexity computational demands. Additionally, non‐Gaussian outliers caused by equipment measurement noise can significantly deviate normal patterns, making them difficult filter using standard methods. To address these challenges, this paper proposes robust, user‐type‐specific anomaly method. After preprocessing, correntropy‐based K‐means clustering method used separate users with noisy data. A two‐stage framework combining fuzzy logic convolutional neural network (CNN)‐long short‐term memory (LSTM) enhances both efficiency accuracy. The experiments were conducted open‐source datasets, results demonstrated that our achieved an accuracy 95%, which approximately 4% higher than traditional Isolation Forest This indicates approach effectively balances detection, its generalizability further validated on additional dataset.

Language: Английский

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Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers

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

Published: March 29, 2025

Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due the high-dimensional nature of EEG signals and subtle temporal patterns drowsiness, there increasing recognition need for deep neural networks (DNNs) capture dynamics drowsy driving better. Meanwhile, optimizing DNNs architectures remains challenge, as training these models an NP-hard problem. Meta-heuristic algorithms offer alternative traditional gradient-based optimizers improving performance. This study investigates use two human-inspired algorithms-teaching learning-based optimization (TLBO) student psychology-based (SPBO)-to optimize convolutional (CNNs) EEG-based detection. Results demonstrate strong predictive performance both CNN-TLBO CNN-SPBO, with area under curve values 0.926 0.920, respectively. TLBO produced simpler model 4,145 parameters, whereas SPBO generated more complex architecture 264,065 parameters but completed faster (116 vs. 148 min). Despite minor overfitting, SPBO's efficiency makes it cost-effective solution. In general, our findings contribute advancement driver monitoring systems road while emphasizing broader role meta-heuristic techniques in learning optimization.

Language: Английский

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Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications

Energy and AI, Journal Year: 2025, Volume and Issue: unknown, P. 100515 - 100515

Published: April 1, 2025

Language: Английский

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Day-Ahead Electricity Price Forecasting for Sustainable Electricity Markets: A Multi-Objective Optimization Approach Combining Improved NSGA-II and RBF Neural Networks

Sustainability, Journal Year: 2025, Volume and Issue: 17(10), P. 4551 - 4551

Published: May 16, 2025

The large-scale integration of renewable energy into power grids introduces substantial stochasticity in generation profiles and operational complexities due to electricity’s non-storable nature. These factors cause significant fluctuations day-ahead market prices. Accurate price forecasting is crucial for participants optimize bidding strategies, mitigate curtailment, enhance grid sustainability. However, conventional methods struggle address the nonlinearity, high-frequency dynamics, multivariate dependencies inherent electricity This study proposes a novel multi-objective optimization framework combining an improved non-dominated sorting genetic algorithm II (NSGA-II) with radial basis function (RBF) neural network. NSGA-II mitigates issues population diversity loss, slow convergence, parameter adaptability by incorporating dynamic crowding distance calculations, adaptive crossover mutation probabilities, refined elite retention strategy. Simultaneously, RBF network balances prediction accuracy model complexity through structural optimization. It verified data Singapore compared other models error calculation methods. results highlight ability track peak adapt seasonal changes, indicating that (NSGA-II-RBF) has superior performance provides reliable decision support tool sustainable operation market.

Language: Английский

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Training LSTMS with circular-shift epochs for accurate event forecasting in imbalanced time series

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121701 - 121701

Published: Sept. 20, 2023

Language: Английский

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Application of neural networks to predict indoor air temperature in a building with artificial ventilation: impact of early stopping

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: July 14, 2024

Language: Английский

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