Ship power load forecasting based on PSO-SVM

Abstract

<abstract> <p>Compared with the land power grid, power capacity of ship power system is small, its power load has randomness. Ship power load forecasting is of great significance for the stability and safety of ship power system. Support vector machine (SVM) load forecasting algorithm is a common method of ship power load forecasting. In this paper, water flow velocity, wind speed and ship speed are used as the features of SVM to train the load forecasting algorithm, which strengthens the correlation between features and predicted values. At the same time, regularization parameter C and standardization parameter σ of SVM has a great influence on the prediction accuracy. Therefore, the improved particle swarm optimization algorithm is used to optimize these two parameters in real time to form a new improved particle swarm optimization support vector machine algorithm (IPSO-SVM), which reduces the load forecasting error, improves the prediction accuracy of ship power load, and improves the performance of ship energy management system.</p> </abstract>

Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

“…Cluster-analysis-based data-mining techniques, such as [11][12][13] High requirements on the quantity of the data; High computational cost Machine learning, such as [14][15][16][17][18][19][20][21][22][23] Strong non-linear mapping ability; Influence of super parameters on prediction stability Artificial intelligence algorithms, such as [24][25][26] Strong convergence; Easy to fall into local extreme value Data envelopment analysis, such as [27][28][29] Strong applicability; Wide application range This study presents a power load forecasting-based abnormal data detection method to improve the economy of electricity inspection and promote the sustainable development of electric power firms. First, an intelligent algorithm is used to optimize the parameters of ELM to improve the forecasting accuracy for the power load.…”

“…Several studies in the field of electricity inspection have used artificial intelligence algorithms to improve data utilization and make the analysis process simpler and more effective. For instance, neural networks (NNs), the autoregressive moving average (ARMA) model, ELM, and the support vector machine (SVM) algorithm have been used in electricity inspection and load detection [14][15][16]. The classic load curve of users is obtained by analyzing the load of users, and the load is monitored and analyzed based on the evaluation method.…”