COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Houran, Mohamad Abou; Bukhari, Syed; Zafar, Muhammad Usman; Mansoor, Majad; Chen, Wenjie

doi:10.1016/j.apenergy.2023.121638

Cited by 79 publications

(23 citation statements)

References 49 publications

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“…The fitness function is used to measure the performance of parameter configurations searched by individual dung beetles. In this paper, the fitness function of the DBO algorithm will consider the mean square error (MSE) [56] of network training, which needs to be minimized during model training. MSE is shown in Equation ( 9)…”

Section: Dbo-cnn-bilstm Prediction Modelmentioning

confidence: 99%

DBO-CNN-BiLSTM: Dung Beetle Optimization Algorithm-Based Thrust Estimation for Micro-Aero Engine

Lei,

Huang,

Chen

et al. 2024

Aerospace

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Thrust constitutes a pivotal performance parameter for aircraft engines. Thrust, being an indispensable parameter in control systems, has garnered significant attention, prompting numerous scholars to propose various methods and algorithms for its estimation. However, research methods for estimating the thrust of the micro-turbojet engines used in unmanned aerial vehicles are relatively scarce. Therefore, this paper proposes a thrust estimator for micro-turbojet engines based on DBO (dung beetle optimization) utilizing bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN). Furthermore, the efficacy of the proposed model is further validated through comparative analysis with others in this paper.

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Section: Dbo-cnn-bilstm Prediction Modelmentioning

confidence: 99%

DBO-CNN-BiLSTM: Dung Beetle Optimization Algorithm-Based Thrust Estimation for Micro-Aero Engine

Lei,

Huang,

Chen

et al. 2024

Aerospace

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“…Aiming at the above problems, this paper combines the forecasting model and wind power data processing to improve forecasting accuracy and proposes a LSTM short-term wind power forecasting model based on data preprocessing and VMD [ 20 ] for the soft sensor. We use the isolation forest and multiple imputation methods to deal with outliers and missing values of wind power data [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors

Lei,

Ma,

Zhu

et al. 2024

Sensors

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Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

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“…Given the respective advantages of the CNN and LSTM, scholars have applied the combination of both to wind power forecasting tasks. The literature [23] proposed a CNN_LSTM forecasting model combined with a swarm intelligence (SI) optimization algorithm to perform a short-term offshore wind power forecasting task, and the experimental results provided accurate wind power prediction data for the management of renewable energy conversion networks. The literature [24] proposed a deep learning model based on CNN-Bi-LSTM and embedded GA optimization for forecasting wind speed for the next 24 h. The internal features of the time series are directly extracted using the CNN, while the Bi-LSTM can fully utilize the upper layer information from both forward and backward directions.…”

Section: Introductionmentioning

confidence: 99%

A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture

Zhang,

Ren,

Liu

2024

Energies

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With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks.

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COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Cited by 79 publications

References 49 publications

DBO-CNN-BiLSTM: Dung Beetle Optimization Algorithm-Based Thrust Estimation for Micro-Aero Engine

DBO-CNN-BiLSTM: Dung Beetle Optimization Algorithm-Based Thrust Estimation for Micro-Aero Engine

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors

A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture

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