With the continuous improvement of the electrification level of smart fishing grounds

the proportion of environmentally sensi?tive loads in fishing grounds increases. Load forecasting should fully consider the impact of environmental climate change on the operationof fishery electrical equipment. Firstly

the data collected by the fishing ground monitoring platform are analyzed and preprocessed

andthe typical scenes are divided according to the climatic characteristics. Secondly

the least square method is used to decompose the meteo?rological load affected by the climate

and the correlation coefficient method is used to analyze the correlation between the load and the me?teorological index. The principal component analysis method is used to transform multiple single meteorological indicators into a few com?prehensive indicators in SPSS. The comprehensive meteorological indicators and meteorological load data are used to train the BP neuralnetwork

and the load forecasting model of fishery electrical equipment based on BP neural network in typical scenarios is constructed. Fi?nally

an example is verified based on the operation of fishery electrical equipment and climate index data on the target day. The resultsshow that the model reflects the impact of environmental climate change on the load of fishery electrical equipment

and the error accuracymeets the actual engineering needs.