Research On Electrical Load Pattern Recognition And Load Forecasting Based On Deep Learning

Electric power is the lifeblood of modern social development and the basic guarantee for economic development,people’s life and social stability.Electric energy is not easy to store.In order to ensure the stable operation of the power grid,it is necessary to keep the real-time power balance between the power generation side and the load side of the power grid,which requires accurate prediction of future power demand and data support for the formulation of power generation plans.However,with a large number of clean energy connected to the power grid and the number of electric vehicles increasing year by year,the complexity and uncertainty of the power grid are deepening,which challenges the traditional power load forecasting model.Therefore,improving the accuracy of power load forecasting can reduce unnecessary power generation,improve energy utilization efficiency,and then reduce the operation cost of power grid,which has great economic benefits.As an end-to-end feature learning model,deep learning can model complex data through multi-layer nonlinear transformation,and has been widely used in many fields.The feature learning of power load data based on deep learning method is expected to improve the accuracy of power load forecasting and meet the demand of current power system.It is a research hotspot at present.Aiming at the demand of load pattern recognition and load forecasting and considering the unbalanced and periodic characteristics of power load data,this paper combines clustering and time series feature learning methods and carries out the following research contents:(1)A power load pattern recognition algorithm based on unbalanced data clustering is presented.On the basis of the traditional fuzzy c-mean algorithm,the fuzzy degree matrix is used to quantify the cluster size and normalize the objective function of the traditional algorithm,and then the renewal formulas of the cluster center and membership degree matrix are derived.The experimental results show that the proposed algorithm can identify the load patterns of holidays from the historical load data set,and is better than the similar algorithms in multiple clustering effectiveness evaluation indicators.Compared with the traditional clustering algorithm,the load forecasting effect on holidays is improved by using multiple regression models.(2)A power load forecasting model based on clustering and periodic enhancement LSTM is proposed.Firstly,the unbalanced data clustering algorithm is used to cluster the historical data sets,and several typical load patterns are obtained.Secondly,a prediction model is established for each load pattern.Considering the periodicity of the load sequence,the ful1 connection layer is constructed by connecting the load value in the input sequence and the output of the LSTM network in the same period with the time to be predicted in series,and the load prediction results are output.The experimental results show that the load forecasting using the load and temperature series of 24 hours before the time to be predicted achieves better forecasting results than the traditional LSTM network and other load forecasting models.The MAPE value on the commonly used North American power data set is 1.51%.(3)A power load forecasting model based on periodic enhancement Informer is proposed.The periodic load values in the input sequence and the output of the traditional Informer model are connected in series to form a full connected layer.Combined with the convolution neural network,the time-series characteristics,local characteristics and periodic characteristics in the long sequence are learned at the same time,thus overcoming the loss of periodic characteristics caused by the probability sparse self attention mechanism in the traditional Informer model.The experimental results show that the accuracy of load forecasting is further improved by taking the load and temperature series of 1 week before the time to be predicted as input,and the MAPE value on the North American power data set is reduced to 1.15%.(4)A probabilistic load forecasting model based on the combination of LSTM and attention mechanism is proposed.First,the Informer model is enhanced with different random initial values and operation periods respectively,and a group of load forecasting results are obtained,and the variance is taken as the model uncertainty.Secondly,the square of the differences between the load mean and the real value is calculated,and the data uncertainty estimation model is established by using the relationship between the LSTM network fitting based on the attention mechanism and the input sequence.Finally,taking the sum of model uncertainty and data uncertainty as the estimation result of load uncertainty,the upper and lower bounds of load under different confidence levels are determined by using the critical value of standard normal distribution.The experimental results show that the proposed model is superior to the traditional Informer model and other similar models in many indexes.To sum up,combining the characteristics of power data and deep learning method,this paper has carried out the research on power load pattern recognition algorithm and load forecasting model,and achieved good results.