Net load forecasting method in distribution grid planning based on LSTM network

Abstract

Distribution grid planning involves multiple nodes, lines, equipment, and other elements. Due to the large scale of the system, there are complex interactions in space. The net load is affected by the load changes of different nodes. If this spatial complexity is not fully considered, the net load prediction results may be inaccurate. Therefore, in order to ensure the effect of net load forecasting, a method of net load forecasting in distribution grid planning based on the Long Short-Term Memory (LSTM) network is proposed. This method fully considers the characteristics of distribution grid planning and constructs a net load forecasting model for distribution grid planning based on the LSTM network. This model selects the 3σ criterion detects and corrects the singular values in the historical load data, and obtains the reasonable maximum time series results of each day; The adaptive noise complete set empirical mode decomposition method is used to decompose the sequence results and generates Intrinsic Mode Function (IMF) components of each time series; According to the component results, a load forecasting model based on LSTM network is constructed, and the initial learning rate and cell number parameters of LSTM network are optimized by improving the Pelican optimization algorithm to improve the precision of load forecasting of LSTM network. The test results show that the method can detect singular values in the data and weaken the impact of grid planning on netload forecasting; It can effectively complete the decomposition of historical load data, and each component after decomposition will not be aliased; The prediction error of net load is less than 1.25%, which can provide a reliable basis for grid planning of distribution network.

Keywords

• lstm network
• distribution network
• grid planning
• medium net load forecasting
• time series
• parameter optimization