A review of the applications of artificial intelligence in renewable energy systems: An approach-based study

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 306, P. 118207 - 118207

Published: March 16, 2024

Language: Английский

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A state of art review on estimation of solar radiation with various models

Heliyon, Journal Year: 2023, Volume and Issue: 9(2), P. e13167 - e13167

Published: Jan. 21, 2023

Solar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, energy production, it plays a critical role in the sustainability of biological, chemical processes nature. In this framework, knowledge solar data or estimating accurately possible vital to get maximum benefit from sun. From point view, many have revised their future investments/plans enhance profit margins sustainable development according knowledge/estimation radiation. This case has noteworthy attracted attention researchers estimation with low errors. Accordingly, noticed that various types models been continuously developed literature. The present review paper mainly centered on works estimated by empirical models, time series, artificial intelligence algorithms, hybrid models. general, these needed atmospheric, geographic, climatic, historical given region It seen literature each model its advantages disadvantages radiation, gives best results one may give worst other region. Furthermore, an parameter strongly improves performance success worsen another direction, separately detailed terms algorithms. research gaps, challenges, directions drawn study. results, well-observed exhibited more accurate reliable studies due ability merge between different model, but come fore ease use, computational costs.

Language: Английский

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A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction

Energy, Journal Year: 2023, Volume and Issue: 275, P. 127430 - 127430

Published: April 8, 2023

Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure the power generation network. To deliver a high-quality prediction, this paper proposes hybrid combination technique, based on deep learning model of Convolutional Neural Networks Echo State Networks, named as CESN. Daily from four sites (Roderick, Rocklea, Hemmant Carpendale), located Southeast Queensland, Australia, have been used to develop proposed prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, neural network, Light Gradient Boosting) compare evaluate outcomes approach. results obtained experimental showed that able obtain highest performance compared existing developed for daily forecasting. Based statistical approaches utilized study, approach presents accuracy among models. algorithm excellent accurate forecasting method, which outperformed state art algorithms are currently problem.

Language: Английский

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Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120392 - 120392

Published: Feb. 21, 2024

Language: Английский

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Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model

Applied Energy, Journal Year: 2024, Volume and Issue: 359, P. 122624 - 122624

Published: Jan. 24, 2024

Wind energy is an environment friendly, low-carbon, and cost-effective renewable source. It is, however, difficult to integrate wind into a mixed grid due its high volatility intermittency. For conversion systems be reliable efficient, accurate speed (WS) forecasting fundamental. This study cascades convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) in order obtain model for hourly WS by utilizing several meteorological variables as inputs their effects on predicted WS. input selection, the mutation grey wolf optimizer (TMGWO) used. efficient optimization of CBiLSTM hyperparameters, hybrid Bayesian Optimization HyperBand (BOHB) algorithm The combined usage TMGWO, BOHB, leads three-phase (i.e., 3P-CBiLSTM). performance 3P-CBiLSTM benchmarked against standalone BiLSTMs, LSTMs, gradient boosting (GBRs), random forest (RFRs), decision tree regressors (DTRs). statistical analysis forecasted reveals that highly effective over other benchmark methods. objective also registers highest percentage errors (≈ 53.4 – 81.8%) within smallest error range ≤ |0.25| ms−1 amongst all tested sites. Despite remarkable results achieved, cannot generally understood, so eXplainable Artificial Intelligence (xAI) technique was used explaining local global outputs, based Local Interpretable Model-Agnostic Explanations (LIME) SHapley Additive exPlanations (SHAP). Both xAI methods determined antecedent most significant predictor forecasting. Therefore, we aver proposed can employed help farm operators making quality decisions maximizing power integration reduced

Language: Английский

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Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction

Measurement, Journal Year: 2022, Volume and Issue: 202, P. 111759 - 111759

Published: Aug. 19, 2022

Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring power systems. However, its stochastic behaviour is a significant challenge achieving satisfactory results. This study aims to design innovative hybrid model that integrates feature selection mechanism using Slime-Mould algorithm, Convolutional-Neural-Network (CNN), Long–Short-Term-Memory Neural Network (LSTM) final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed was applied six farms Queensland (Australia) at daily temporal horizons different time steps. comprehensive benchmarking of the obtained results those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) three Machine-Learning (Artificial-Neural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted higher performance all selected farms. From obtained, this work establishes designed SCLC could have practical utility for applications renewable sustainable energy resource management.

Language: Английский

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Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

Cognitive Computation, Journal Year: 2022, Volume and Issue: 15(2), P. 645 - 671

Published: Nov. 7, 2022

Language: Английский

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Predictive models development using gradient boosting based methods for solar power plants

Journal of Computational Science, Journal Year: 2023, Volume and Issue: 67, P. 101958 - 101958

Published: Jan. 31, 2023

Language: Английский

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Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting

Applied Energy, Journal Year: 2023, Volume and Issue: 353, P. 122059 - 122059

Published: Oct. 18, 2023

Prediction of electricity price is crucial for national markets supporting sale prices, bidding strategies, dispatch, control and market volatility management. High volatility, non-stationarity multi-seasonality prices make it significantly challenging to estimate its future trend, especially over near real-time forecast horizons. An error compensation strategy that integrates Long Short-Term Memory (LSTM) network, Convolution Neural Network (CNN) the Variational Mode Decomposition (VMD) algorithm proposed predict half-hourly step prices. A prediction model incorporating VMD CLSTM first used obtain an initial prediction. To improve predictive accuracy, a novel framework, which built using Random Forest Regression (RF) algorithm, also used. The VMD-CLSTM-VMD-ERCRF evaluated from Queensland, Australia. results reveal highly accurate performance all datasets considered, including winter, autumn, spring, summer, yearly predictions. As compared with without (i.e., VMD-CLSTM model), outperforms benchmark models. For predictions, average Legates McCabe Index seen increase by 15.97%, 16.31%, 20.23%, 10.24%, 14.03%, respectively, relative According tests performed on independent datasets, can be practical stratagem useful short-term, forecasting. Therefore research outcomes demonstrate framework effective decision-support tool improving accuracy price. It could value energy companies, policymakers operators develop their insight analysis, distribution optimization strategies.

Language: Английский

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Probabilistic-based electricity demand forecasting with hybrid convolutional neural network-extreme learning machine model

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 107918 - 107918

Published: Feb. 3, 2024

Language: Английский

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