Multi-step Load Forecasting Of Distribution Network Based On Bidirectional Long Short-Term Memory Networks And Attention Mechanism

Load forecasting is one of the key technologies in the construction of smart grids.The implementation of high-precision load forecasting is crucial for ensuring the safe and stable operation of the power grid,as well as improving energy efficiency.Distribution grid load forecasting can further assist in grid planning and design,optimize power system operations,and conserve energy resources.This paper focuses on short-term load forecasting in distribution grids.It addresses the issue of noise in load data by employing a stacked denoising autoencoder with residual connections.Furthermore,the accuracy of two-step and three-step load forecasting is further improved by combining an attention mechanism with a bidirectional long short-term memory network.The proposed methods are validated using a publicly available dataset as a case study.Finally,the importance of multi-step load forecasting for distribution grid planning is demonstrated through economic dispatch studies.Firstly,in order to address the issues of poor accuracy and instability in load forecasting for power distribution networks caused by noise during data collection and transmission,a data processing model based on stacked denoising autoencoders and residual networks is designed.The model is combined with a bidirectional long short-term memory network through residual connections.The stacked denoising autoencoders train the model by introducing noise into the input data,enabling multi-level learning and reconstruction of the input data to effectively remove noise and restore clean data.The residual connections allow information to directly skip between different layers in the network,which helps accelerate information propagation and enables the model to better capture feature representations at different levels.A comparison with traditional methods reveals that the combined model improves various metrics by approximately 5%,while maintaining similar fitting accuracy.Once again,in order to address the issues of low accuracy in the second and third-step predictions and poor fitting performance at peak points in the multi-step prediction of the distribution network,an attention mechanism is embedded into a bidirectional long short-term memory network.By introducing new trainable variables,the network becomes more flexible and learns different attention distributions at various levels within the network,thereby enhancing the connections between different segments of data.Experimental results demonstrate that the improved method particularly enhances the accuracy of the second and third-step predictions,reducing the error by approximately 20%.Moreover,the coefficient of determination(R-squared)exhibits an increase of about 9% in the third step.Additionally,the fitting performance at peak points is also improved.Finally,further research is conducted to verify the impact of multi-step prediction on distribution network planning and optimization scheduling.An improved particle swarm optimization algorithm is designed to optimize the search process,which is prone to getting trapped in local optima.The inertia weight and learning factor are dynamically adjusted to enhance the search capability.Experimental results demonstrate that accurate load prediction ensures stable economic scheduling of the distribution network.Moreover,the improved scheduling algorithm reduces the operating costs by approximately $1300,representing a decrease of about 2%.In conclusion,this paper provides insights into the handling of continuous missing values and improvement of prediction accuracy in multi-step prediction.Furthermore,the study on optimization scheduling confirms the importance of load multi-step prediction in distribution network operation planning.