In this paper, a forecasting method for day -ahead photovoltaic (PV) generation using long shortterm memory (LSTM) neural network is presented. To improve the quality of the predictions, the most relevant hyperparamet...
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In this paper, a forecasting method for day -ahead photovoltaic (PV) generation using long shortterm memory (LSTM) neural network is presented. To improve the quality of the predictions, the most relevant hyperparameters of the proposed model are adjusted using a bayesian optimization algorithm. The study uses a database obtained from a grid -connected solar plant containing historical data of the PV power generated, solar irradiance, ambient temperature, PV panel temperature and wind speed. First, a preprocessing algorithm is applied to enhance the quality of the data and the accuracy of the forecasts. Subsequently, the proposed model is used to predict the power generated at the facility for the following day. The obtained results are compared with other types of models used in the related literature: a gated recurrent unit (GRU) neural network and a multilayer perceptron (MLP) neural network. The tests are performed on days with different meteorological behavior, and it is observed that the proposed model outperforms the comparison models in all cases analyzed in terms of accuracy and quality of predictions.
bayesian optimization algorithm (BOA) utilizes a bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed bayesian network. This pape...
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bayesian optimization algorithm (BOA) utilizes a bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is effectively preserved. This leads to a better search when the diversity of population is high at the beginning of search. Moreover, it tunes the solutions by automatically decreasing the number of clusters as the diversity of population decreases during the search process. The experimental results demonstrate that the proposed algorithm achieves better performance on well-known benchmark functions in the continuous global optimization.
The rock burst belongs to a serious engineering geological disaster among the underground and deep resource exploitation engineering. The prediction of rock burst intensity has become an urgent problem to be addressed...
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The rock burst belongs to a serious engineering geological disaster among the underground and deep resource exploitation engineering. The prediction of rock burst intensity has become an urgent problem to be addressed in underground engineering. In order to effectively predict the rock burst intensity grades, the four indicators were comprehensively selected in this paper to establish a rock burst evaluation indicator system. More specifically, the bayesian optimization algorithm was employed to optimize the XGBoost machine learning algorithm to determine the weights of various evaluation indicators. Afterwards, the cloud model theory was introduced into the grade prediction study of rock burst intensity, as well as ultimately establishing a comprehensive evaluation model of rock burst intensity based on the BO-XGBoost-Cloud model. Consequently, the feasibility and validity of the model were examined with the assistance of on-site measured data and rock burst discrimination results among the relevant literature, which indicated that: the BO- XGBoost-Cloud model exhibited the highest accuracy in predicting rock burst intensity, significantly surpassing the extension evaluation method and the entropy-cloud model. Therefore, the BO-XGBoost-Could model was the optimal model for predicting rock burst intensity, which provided a creative forecasting method for the classification prediction of rock burst intensity in underground engineering.
In-service urban utility tunnels (UUT) suffer from cracks, corrosion, and leakage defects, which rises the chance of major accidents. However, prevailing detection methods for UUT remain reliant on manual inspection a...
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In-service urban utility tunnels (UUT) suffer from cracks, corrosion, and leakage defects, which rises the chance of major accidents. However, prevailing detection methods for UUT remain reliant on manual inspection and subjective judgment, or traditional image processing technologies, such methods may not be able to obtain accurate defect information. This study proposes a novel and effective network called UUTNet based on the constructed UUT dataset for defects detection. Considering that the UUT defects has a certain distribution correlation, the attention module is introduced to the Pyramid Scene Parsing Network to capture the relation. By adding the hybrid dilated convolution after the feature extraction layer, the receptive field is expanded to further extract global and local features. The performance of UUTNet was evaluated based on the metrics MIoU, F1-score, Accuracy, and robustness. Comparative experiments were conducted, and the results showed the UUTNet achieved the best detection performance, achieving 0.7615 MIoU, 0.9806 Accuracy and 0.8012 F1-score. The MIoU was further improved to 0.7847 by utilizing the bayesianoptimization. Three extreme inspection scenes, including uneven illumination, high brightness, and obstacle interference, were applied to validate model robustness. The proposed method offers robust technical assistance for detecting defects in the UUT and precisely assessing the distribution and extent of these defects.
Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study intro...
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Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.
Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of da...
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Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two-stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long-short term memory, which is optimized by the bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10-min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.
This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that hav...
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This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a bayesian optimization algorithm (BOA) to enhance the model's hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R2 values for all the developed models are > 0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA-NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA-SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies.
The highly non-linear and nonstationary nature of runoff events in changing environments makes accurate and reliable runoff forecasting difficult. We propose a hybrid model by integrating an autoregressive (AR) model,...
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The highly non-linear and nonstationary nature of runoff events in changing environments makes accurate and reliable runoff forecasting difficult. We propose a hybrid model by integrating an autoregressive (AR) model, bayesian inference, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, bayesianoptimization, and support vector regression. Two bayesian inference methods (the No-U-Turn Sampler (NUTS) and variational inference) were used to calculate the parameters of the AR model to obtain a bayesian AR (BAR) model. Credible intervals were used to analyse the uncertainty of the parameters and model prediction results. The above model is applied to the daily runoff predictions of hydrological stations in the Yellow River basin of China. The results show that (1) the hybrid model can improve the prediction accuracy and (2) the NUTS algorithm-based model provides a narrower reliable interval and performs better in uncertainty analyses.
Accurate short-term load forecasting can ensure the reliable power supply of the power system and the regular operation of the grid economy. This article proposes a hybrid model based on convolutional neural networks ...
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Accurate short-term load forecasting can ensure the reliable power supply of the power system and the regular operation of the grid economy. This article proposes a hybrid model based on convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) with bayesianoptimization (BO) and attention mechanism (AM) for short-term load forecasting. CNN is used to capture the significant features of input data. BiLSTM is adept in time series forecasting and AM can reduce the computational complexity of model. BO can help to tune the hyperparameters automatically. The input features of models are recent load, time slot, date type, and meteorological factors. In order to eliminate the seasonality, the data set is divided into four subsets according to four seasons. The performance of the proposed model is compared with other models by MAE, RMSE, MAPE, and R2 score. The forecasting results represent that the proposed model is most suitable for short-term load forecasting among the contrast models and the meteorological factors have an impact on forecasting accuracy.
Water pollution is a serious environmental problem with a significant negative impact on human health and the ecosystem. Adsorption is an attractive process for water decontamination. Developing artificial intelligenc...
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Water pollution is a serious environmental problem with a significant negative impact on human health and the ecosystem. Adsorption is an attractive process for water decontamination. Developing artificial intelligence models capable of predicting the adsorption performance of newly developed materials could save huge amounts of resources and efforts. In the present study, a novel polyethyleneimine/graphene oxide/layered triple hy-droxide (PEI/GO/LTH) nanocomposite was synthesized, characterized, and applied to adoptively remove harmful phenolic (bisphenol A) and azo dye (acid red 1) from wastewater samples. The results revealed that the PEI/GO/LTH nanocomposite is highly effective in removing these two pollutants. The adsorption isotherms and kinetics of these two pollutants are best fitted by the Langmuir isotherm and pseudo-second-order models, respectively. Additionally, the synthesized nanocomposite is easily and highly regenerable with an insignificant loss in performance when repeatedly used. Besides the above investigations, the present study also employs support vector machines (SVM) and bayesianoptimization as tools for predicting the adsorptive removal of acid red 1 (AR1) dye and bisphenol A (BPA) from contaminated water samples by the synthesized PEI/GO/LTH nanocomposite. The models achieved promising results as the correlation coefficients, during the testing phase, reached 97.3 and 96.6 % for the AR1 and BPA data using BO-SVM models, respectively.
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