Research And System Implementation Of Load Forecasting Method For Power Consumption Side Based On Memory Unit And Convolution Optimizatio

With the increase in household electricity consumption,the structure of energy and electricity consumption has also undergone significant changes.The resumption of business and production in society after the epidemic has brought many uncertainties to the stable operation of the power grid.Accurate load forecasting can formulate reasonable power supply planning for the power supply side and ensure the stable operation of the power grid.Currently,with the increase in the category of electrical appliances and the increase in electricity demand,traditional load forecasting algorithms cannot meet the needs of various electricity scenarios due to their low prediction accuracy and poor model applicability.Compared to traditional load forecasting methods,the forecasting method based on deep learning can be applied to various complex power consumption scenarios,and can fully explore various rules in load data.However,forecasting algorithms based on deep learning still have significant shortcomings in terms of time loss and forecasting accuracy.In response to the above issues,this article focuses on the structural design and optimization of load forecasting models based on deep learning,as well as the design and implementation of load and energy management systems,and carries out relevant research and analysis.The specific research content is as follows:(1)Aiming at the problem of high time consumption of existing deep learning algorithms for short-term load forecasting,a short-term load forecasting method oriented to memory cell and residual convolution optimization was proposed.The method first utilizes a wavelet decomposition strategy to decompose the input data.Combined with void convolution,self attention module,and long and short term memory network,a stacked memory unit module is constructed to optimize feature extraction of load data.Then,a load forecasting model(RST-LSTM)for memory cell and residual convolution optimization is constructed based on a stack memory cell and a residual combination optimization structure.Finally,the convolutional output of each layer is activated through a mode of enhancing convolutional connections.The experimental results show that compared with current deep learning methods,this method has significant optimization in terms of time loss and forecasting accuracy for short-term load forecasting.(2)Aiming at the problem of low accuracy of existing deep learning algorithms for forecasting extreme values in medium to long term load forecasting,a medium to long term load forecasting method oriented to memory cell and graph convolution optimization is proposed.The method first uses Kalman filtering to denoise the input data.Then,the graph convolution feature analysis optimization module and the encoding decoding feature enhancement output module are used to construct a dual branch load forecasting model.The graph convolution feature analysis and optimization module consists of a graph generation method and a graph neural network to achieve global feature extraction of load data from a spatial perspective;The encoding decoding feature enhancement output module is composed of the encoding decoding structure network of the long and short term memory network and the attention mechanism,which realizes the local feature extraction of the load data from a time perspective.Finally,the weighted loss function is combined to optimize the forecasting and analysis of extreme values.Experimental results show that this method has a better improvement in extreme value forecasting compared to current deep learning methods for medium to long-term load forecasting.