A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems

Akhter, Muhammad Naveed; Mekhilef, Saad; Mokhlis, Hazlie; Raza, Ali; Usama, Muhammad; Muhammad, Munir Azam; Khairuddin, Anis Salwa Mohd

doi:10.1016/j.apenergy.2021.118185

Cited by 60 publications

(19 citation statements)

References 54 publications

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“…presented a hybrid prediction model based on improved convolutional neural network and bidirectional gated recurrent unit for predicting solar generated power [19]. developed a hybrid version of deep learning (DL) method (SSA-RNN-LSTM) for an hour-ahead prediction of three different PV systems [20].…”

Section: Plos Onementioning

confidence: 99%

“…Among the machine learning models, few feed forward models and their variants, recurrent neural predictors and memory based models has been widely used [11][12][13][14][15][16][17][18]. Also, with the growth of deep learning based techniques, researchers has initiated in developing predictor models for solar PV output power forecasting using various deep learning models for the said application [1,14,19,20,26,27,43,47]. On this detailed review made on the different machine learning and deep learning models for PV output power forecasting of solar farms, they are prone to possess the disadvantages as listed below,…”

Section: Challengesmentioning

confidence: 99%

“…• Occurrences of global minima and stagnation issues [3][4][5][6][7] • Scalability problems on the normalization procedures adopted [2,8,[12][13][14][15][16][17] • Over-fitting and under-fitting issues [5, 6, 9-11, 23, 48, 51] • Dimensionality constraints of the solar farm data and data handling issues [18][19][20][21][22][23][24] • Elapsed training time [29,31,37] • Data extraction problems in regression based ML models [10][11][12][13][14][15] • Higher number of trainable parameters in DL models [1, 14, 19-20, 26, 27, 43, 47] • Repetitive training of deep neural networks [19,20,26,27] • High computational overhead due to repetitive process [29][30][31][32][33][34][35][36] • Few predictor models with high complexity and data redundancy [45][46][47]…”

Section: Challengesmentioning

confidence: 99%

“…The solution to the defined problem with Lagrange multiplier of Eq (20) determines the hyperplanes based on the fuzzy membership functions μ 1 and μ 2 . The modelled DLFTSVM is designed with the deep dense SVM layers and the pooling layers and the auto encoder and decoder units transforms all the input data points to low dimensional data components.…”

Section: Plos Onementioning

confidence: 99%

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Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

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Section: Plos Onementioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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“…DL is an advanced branch of machine learning with the ability to process non-linear and complex relationships between various inputs and the forecasted However, variations in solar irradiance and meteorological conditions cause fluctuation in solar power generation and lead to uncertainty in power output. This results in a power imbalance between the demand and the supply side of the grid [6]. Also, the unpredictable output significantly impacts the economic dispatch and the scheduling, stability, and reliability of the power system operation [7].…”

Section: Introductionmentioning

confidence: 99%

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Santos¹,

García-Santiago²,

Camarero³

et al. 2022

Energies

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The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities.

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Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

Wang,

Yang,

Zhou

et al. 2023

Energy Science & Engineering

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As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

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A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems

Cited by 60 publications

References 54 publications

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

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