Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 158, P. 111557 - 111557

Published: April 1, 2024

Language: Английский

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Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and Methods

Smart Cities, Journal Year: 2025, Volume and Issue: 8(1), P. 25 - 25

Published: Feb. 7, 2025

The ongoing increase in urban populations has resulted the enduring issue of traffic congestion, adversely affecting quality life, including commute duration, road safety, and local air quality. Consequently, recognizing forecasting underlying congestion patterns have become essential, with Traffic Congestion Prediction (TCP) emerging as an increasingly significant area study. Advancements Machine Learning (ML) Artificial Intelligence (AI), well improvements Internet Things (IoT) sensor technologies made TCP research crucial to development Intelligent Transportation Systems (ITSs). This review examines advanced TCP, emphasizing innovative methods their importance for ITS sector. paper provides overview statistical, ML, Deep (DL) approaches, ensembles that compose TCP. We examine several discuss relative absolute evaluation metrics from regression classification perspectives. Finally, we present overall step-by-step standard methodology is often utilized problems. By combining these elements, this highlights critical advancements challenges providing robust detailed information state-of-the-art solutions.

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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Hybrid BiGRU‐CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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

Published: Jan. 1, 2025

ABSTRACT Integrating renewable energy sources into smart grids increases supply and demand management because are intermittent variable. To overcome this type of challenge, short‐term load forecasting (STLF) is essential for managing energy, demand‐side flexibility, the stability with integration. This paper presents a new model called BiGRU‐CNN to improve operation STLF in grids. The integrates bidirectional gated recurrent units (BiGRUs) temporal dependencies convolutional neural networks (CNNs) extract spatial patterns from consumption data. newly developed BiGRU captures past future contexts through processing, CNN component extracts high‐level features enhance accuracy prediction. compared two other hybrid models, CNN‐LSTM CNN‐GRU, on real‐world data American electric power (AEP) ISONE datasets. Simulation results show that proposed outperforms single‐step yielding root mean square error (RMSE) 121.43 123.57 (ISONE), absolute (MAE) 90.95 62.97 percentage (MAPE) 0.61% 0.41% (ISONE). For multi‐step forecasting, yields RMSE 680.02 581.12 MAE 481.12 411.20 MAPE 3.27% 2.91% can generate accurate reliable STLF, which useful massive energy‐integrated

Language: Английский

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Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 2, 2024

Renewable integration in utility grid is crucial the current energy scenario. Optimized utilization of renewable can minimize consumption from grid. This demands accurate forecasting contribution and planning. Most researches aim to find a suitable model terms accuracy error metrics. However, uncertainty variability these forecasts are also significant. work combines point forecast with interval provide comprehensive information about uncertainty. In this work, solar irradiance carried out using artificial intelligence (AI) techniques. Forecasting done seasonal auto-regressive moving average exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques performance evaluated. SVR exhibited best R

Language: Английский

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Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(5), P. e0299603 - e0299603

Published: May 10, 2024

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, short-term using 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) feature selection, model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated (BiGRU) to optimize predictive accuracy. We used 2016 monitoring from Beijing, China empirical basis this compared several deep learning frameworks. RNN, LSTM, GRU, other models based on respectively. The experimental results show that proposed in question are more accurate than those models, R2 paper improves by nearly 5 percentage points single model; reduces MAE points; RMSE 11 points. is predicting PM2.5.

Language: Английский

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Improving short-term forecasting of solar power generation by using an EEMD-BiGRU model: A comparative study based on seven standalone models and six hybrid models

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: 21(14), P. 3135 - 3158

Published: May 27, 2024

Accurate and timely forecasting is critical for grid-connected solar power safety stability, achieved through machine learning (ML) both common real-time applications. To mitigate the impact of nonstationarity volatility in generation, we employed empirical mode decomposition (EMD), ensemble (EEMD), variational (VMD), complete EEMD with adaptive noise (CEEMDAN) to decompose time series into frequency components, reducing fluctuations noise. A combination four methods (EMD, EEMD, VMD, CEEMDAN) two ML models, bidirectional gated recurrent unit (BiGRU) long short-term memory (BiLSTM) were utilized construct six hybrid models (EMD-BiLSTM, EMD-BiGRU, EEMD-BiLSTM, EEMD-BiGRU, VMD-BiGRU, CEEMDAN-BiGRU), which validated on a dataset from 20 MW station Hebei, compared seven standalone backpropagation neural networks (BPNN), support vector machines (SVM), (LSTM), (GRU), convolutional (CNN), BiLSTM, BiGRU, these demonstrated enhanced forecast accuracy. Of these, CEEMDAN-BiGRU significantly reduced prediction errors, percentage reductions root mean square error (RMSE), absolute (MAE), (MAPE) ranging 44.18 ~ 49.43%, 43.67 48.59%, 44.64% ~53.53%, respectively. The EEMD-BiGRU model outperformed all achieving an RMSE 0.7662, MAE 0.3990, MAPE 7.982%, R2 0.9865. findings this study can provide insights applying based generation.

Language: Английский

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Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction

Energy Exploration & Exploitation, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 25, 2024

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial various application domains where real-world data often exhibit complex, non-linear patterns. Our approach advocates utilizing long short-term memory (LSTM) bidirectional (Bi-LSTM) models precise forecasting. To ensure a fair evaluation, we compare performance our proposed with traditional neural networks, time-series methods, conventional decline curves. Additionally, individual based LSTM, Bi-LSTM, other machine learning methods implemented comprehensive assessment. Experimental results consistently demonstrate that model outperforms all benchmarking in terms mean absolute error (MAE) across most datasets. Addressing imbalance between activations by consumer prosumer groups, predictions show superior compared several such as autoregressive integrated moving average (ARIMA) seasonal (SARIMA) model. Specifically, root square (RMSE) Bi-LSTM is 5.35%, 46.08%, 50.6% lower than ARIMA, SARIMA, respectively, May test data.

Language: Английский

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Prediction of Short-Term Solar Irradiance Using the ProbSparse Attention Mechanism for a Sustainable Energy Development Strategy

Sustainability, Journal Year: 2025, Volume and Issue: 17(3), P. 1075 - 1075

Published: Jan. 28, 2025

Sustainability refers to a development approach that meets the needs of present generation without compromising ability future generations meet their own needs. Solar energy is an inexhaustible and renewable resource. From perspective resource utilization, solar power has high degree sustainability. Therefore, one most important ways transform structure promote sustainable economy society, it great significance for promoting construction resource-conserving environmentally friendly society. However, resources also exhibit strong unpredictability; therefore, this paper proposes novel artificial intelligence (AI) model short-term irradiance prediction in photovoltaic generation. Leveraging ProbSparse attention mechanism within encoder-decoder architecture, AI efficiently captures both short- long-term dependencies input sequence. The dingo algorithm innovatively redesigned optimize hyperparameters proposed model, enhancing convergence. Data preprocessing involves feature selection based on mutual information, multiple imputations data cleaning, median filtering. Evaluation metrics include mean absolute error (MAE), root square (RMSE), coefficient determination (R2). demonstrates improved efficiency robust performance prediction, contributing advancements management electrical systems.

Language: Английский

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Bionic fusion perspective: Audiovisual-motivated integration network for solar irradiance prediction

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 314, P. 118726 - 118726

Published: June 27, 2024

Language: Английский

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