Efficient daily electricity demand prediction with hybrid deep-learning multi-algorithm approach

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 297, P. 117707 - 117707

Published: Oct. 5, 2023

Predicting electricity demand (G) is crucial for grid operation and management. In order to make reliable predictions, model inputs must be analyzed predictive features before they can incorporated into a forecast model. this study, hybrid multi-algorithm framework developed by incorporating Artificial Neural Networks (ANN), Encoder-Decoder Based Long Short-Term Memory (EDLSTM) Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICMD). Following the partitioning of data, G time-series are decomposed multiple using ICEEMDAN algorithm, partial autocorrelation applied training sets determine lagged features. We combine where components highest frequency predicted an ANN model, while remaining EDLSTM To generate results, all IMF components' predictions merged ICMD-ANN-EDLSTM models. A comparison made between objective standalone models (ANN, RFR, LSTM), (CLSTM), three decomposition-based on Relative Mean Absolute Error at Duffield Road substation was ≈2.82%, ≈4.15%, ≈3.17%, ≈6.41%, ≈6.60%, ≈6.49%, ≈6.602%, compared ICMD-RFR-LSTM, ICMD-RFR-CLSTM, LSTM, CLSTM, ANN. According statistical score metrics, performed better than other benchmark Further, results show that not only detect seasonality in but also predict analyze market add valuable insight analysis.

Language: Английский

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Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables

Energy and AI, Journal Year: 2023, Volume and Issue: 14, P. 100302 - 100302

Published: Sept. 23, 2023

This paper develops a trustworthy deep learning model that considers electricity demand (G) and local climate conditions. The utilises Multi-Head Self-Attention Transformer (TNET) to capture critical information from G, attain reliable predictions with (rainfall, radiation, humidity, evaporation, maximum minimum temperatures) data Energex substations in Queensland, Australia. TNET is then evaluated models (Long-Short Term Memory LSTM, Bidirectional LSTM BILSTM, Gated Recurrent Unit GRU, Convolutional Neural Networks CNN, Deep Network DNN) based on robust assessment metrics. Kernel Density Estimation method used generate the prediction interval (PI) of forecasts derive probability metrics results show developed accurate for all substations. study concludes proposed predictive tool has high accuracy low errors could be employed as stratagem by modellers energy policy-makers who wish incorporate climatic factors into patterns develop national market insights analysis systems.

Language: Английский

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Short-term ship roll motion prediction using the encoder–decoder Bi-LSTM with teacher forcing

Ocean Engineering, Journal Year: 2024, Volume and Issue: 295, P. 116917 - 116917

Published: Jan. 31, 2024

The safety of maritime operations has become a paramount concern with the advancement intelligent ships. Ship stability and are directly impacted by roll motion, making prediction short-term ship motion pivotal for assisting navigators in timely adjustments averting hazardous conditions. However, predicting poses challenges due to nonlinear dynamics. This study aims predict leveraging encoder–decoder structure Bidirectional Long Short-Term Memory Networks (Bi-LSTM) teacher forcing. model is accomplished employing an map input sequences output varying lengths, forcing enhance network’s ability extract information. To refine analyze model, aspects such as quantity training data guarantee generalization, establishing apposite length relationships between sequences, assessing performance various sea states investigated. Additionally, comparative experiments intervals 10s, 30 s, 60 120 s conducted substantiate necessity effectiveness proposed network. dataset originates from commercial professional simulator developed Norwegian company Offshore Simulator Center AS (OSC).

Language: Английский

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Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 135, P. 108702 - 108702

Published: June 10, 2024

Language: Английский

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Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach

Applied Energy, Journal Year: 2024, Volume and Issue: 374, P. 123920 - 123920

Published: July 31, 2024

Digital technologies with predictive modelling capabilities are revolutionizing electricity markets, especially in demand-side management. Accurate price prediction is essential deregulated markets; however, developing effective models challenging due to high-frequency fluctuations and volatility. This study introduces a hybrid system that addresses these challenges through comprehensive data processing framework for half-hourly predictions. The preprocessing stage employs the Maximum Overlap Discrete Wavelet Transform (MoDWT) enhance input quality by reducing overlap revealing underlying patterns. model integrates Convolutional Neural Networks Random Vector Functional Link (CRVFL) deep learning approach. Bayesian Optimization fine-tunes MoDWT-CRVFL optimal performance. Validation of conducted using prices from New South Wales. results highlight efficacy model, achieving high accuracy superior Global Performance Indicator (GPI) values approximately 1.61, 1.33, 1.85, 1.30, 0.78 Summer, Autumn, Winter, Spring, Annual (Year 2022), respectively, outperforming alternative models. Similarly, Kling–Gupta Efficiency (KGE) metrics proposed consistently surpassed those both decomposition-based standalone For instance, KGE value was 0.972, significantly higher than 0.958, 0.899, 0.963, 0.943, 0.930, 0.661, 0.708, 0.696, 0.739, 0.738 MoDWT-LSTM, MoDWT-DNN, MoDWT-XGB, MoDWT-RF, MoDWT-MLP, Bi-LSTM, LSTM, DNN, RF, XGB, MLP, respectively. methodologies this optimize energy resource allocation, market prices, network management, empowering operators make informed decisions resilient efficient market.

Language: Английский

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