In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper propos...
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In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data;on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments;the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.
In the electricity market environment, price forecast has the characteristics of periodicity and unpredictability. Based on these characteristics, this paper proposes a sparrow-algorithm-optimized support vector machi...
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ISBN:
(纸本)9789811697357;9789811697340
In the electricity market environment, price forecast has the characteristics of periodicity and unpredictability. Based on these characteristics, this paper proposes a sparrow-algorithm-optimized support vector machine (SSA-SVM) model to predict short-term electricity prices. The sparrow algorithm (SSA) has high convergence performance and local search ability, and the parameters of SVM are optimized, the SSA-SVM prediction model is established, and the electricity price data is predicted and analyzed, and then compared with the simulation results of the SSA-SVM model. Finally, the SSA-SVM method has a good effect, error, and convergence speed of the SSA-SVM model are improved.
To address the issue of the low utilization rate of mold platforms in the production line of precast concrete components, a method combining an improved sparrow algorithm with the lowest horizontal line algorithm is p...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
To address the issue of the low utilization rate of mold platforms in the production line of precast concrete components, a method combining an improved sparrow algorithm with the lowest horizontal line algorithm is proposed. Firstly, a layout model for precast concrete components considering the reinforcement process is established. Subsequently, the improved sparrow algorithm is employed to optimize the production sequence of components, and the lowest horizontal line algorithm is utilized to determine the optimized layout positions of the components. By introducing a cubic chaotic sequence to initialize the sparrow population, setting a similarity mutation operator, and applying the cauchy mutation to similar individuals, the sparrow algorithm is enhanced. This enhancement ensures a more even distribution of the sparrow population in the solution space, thereby strengthening the global optimization capability of the sparrow algorithm. Finally, the effectiveness of the proposed method is validated through simulation tests.
With the regular development of the global epidemic, the global port shipping supply is tight. The problem of port congestion, soaring freight rates, and hard-to-find container space has emerged. This paper proposes a...
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With the regular development of the global epidemic, the global port shipping supply is tight. The problem of port congestion, soaring freight rates, and hard-to-find container space has emerged. This paper proposes a new joint berth-quay crane allocation model, namely E-B&QC, by taking the minimum of the time in the port of the ship, the cost of extra transportation distance for collector trucks in the land area of the port, and the cost of extra waiting time for ships. Then, the deficiencies of the sparrow search algorithm (SSA) are considered in solving the E-B&QC model, and the SSA is improved based on the three-dimensional Cat chaos mapping and quantum computing theory. Chaotic Quantum sparrow Search algorithm (CQSSA) is proposed, population individual coding rules are formulated, also E-B&QC model solving algorithm is established. Finally, a new berth-crane allocation optimization method, namely, E-B&QC-CQSSA, is proposed. Subsequently, the feasibility and superiority of the proposed allocation model and solution algorithm are tested according to the actual data of a small river port in the south and a medium-sized river port in the north. Simulation examples show that the E-B&QC model can develop different high-quality solutions for container ports under different working conditions, and the more complex the actual situation of the port, the more significant the optimization effect. The proposed CQSSA for E-B&QC model can obtain a better solution.
In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)-sparrow search algorithm (SSA)-support vector regression machine (SVR)-kernel density estimation...
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In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)-sparrow search algorithm (SSA)-support vector regression machine (SVR)-kernel density estimation (KDE) model to forecast China's future energy consumption in 2022-2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions.
One of the prerequisites for the stable operation of the power system is to ensure the transient stability of the power system. At present, many intelligent algorithms are applied to the transient stability assessment...
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One of the prerequisites for the stable operation of the power system is to ensure the transient stability of the power system. At present, many intelligent algorithms are applied to the transient stability assessment of power systems, but there are still some problems, such as poor effectiveness and low accuracy due to huge data. Aiming at these problems, this paper uses the information entropy-based rough set for attribute dimensionality reduction, filters unnecessary attributes, and obtains a simplified data set. Since the prediction accuracy of the traditional extreme learning machine is not very high, this paper adopts the improved sparrow algorithm to optimize the extreme learning machine, and obtains a high accuracy. Finally, the IEEE39 system simulation results show that the method proposed in this paper can effectively reduce the data dimension, and can quickly and accurately discriminate the transient and stable state of the power system.
The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition numbe...
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The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition number K and penalty factor of variational mode decomposition (VMD) by using the sparrow intelligent algorithm, decomposes the power sequence in a power mode by using the optimised VMD decomposition method and sends all sub-components to a long short-term memory (LSTM) network for prediction.
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