A Method for Medium and Long Term Electricity Consumption Forecasting in Regional Distribution Networks

Abstract

Medium-term and long-term load forecasting is the basic data for formulating medium-term and long-term system maintenance plans and generation schedulings in the power system, and its prediction accuracy is crucial for the rationality of plan formulation. With the development of new energy generation units, a lot of distributed generation devices on the user side are connected to the distribution network, which harms the accuracy of load forecasting on distribution side. Therefore, a more accurate medium-term and long-term power forecasting method for the distribution network is needed. It is a primary problem for state grid to lack the historical electricity date. In response to this, some monthly electricity data are expanded by rolling daily electricity data. The diversity and multi-scale characteristics of factors affecting medium-term and long-term electricity demand limit the predictive performance of a single method. In response to this, a set empirical mode decomposition based on medium and long-term load electricity demand combination prediction method is proposed. Based on the fluctuation characteristics and patterns of each component after data decomposition, suitable prediction algorithms are used to predict each component separately, and finally, the predicted values of electricity demand are synthesized. The calculation results show that the prediction accuracy is effectively improved by the method of this paper.