Short-term load forecasting method and system based on echo state network

Abstract

The invention provides a short-term load forecasting method based on an echo state network, which comprises the steps of collecting historical load data and information of load influencing factors; preprocessing the historical load data; screening out similar days which are similar to a day to be forecasted by using a fuzzy clustering analysis method based on the information of the load influencing factors; building an echo state network load forecasting model based on the preprocessed historical load data of the similar days; and performing load forecasting on the day to be forecasted based on the echo state network load forecasting model. According to the invention, the load influencing factors are considered, the historical similar days are screened out, and the data of the historical similar days is used as training samples, so that the forecasting accuracy of the forecasting model is greatly improved; and meanwhile, by the forecasting model is trained by adopting an L1 / 2 norm regularization method, the generalization ability of the forecasting model is enhanced, and the accuracy of the forecasting result is further improved. The invention further discloses a short-term load forecasting system based on the echo state network.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a short-term load forecasting method and system based on an echo state network. Background technique [0002] Power load forecasting is an important part of the energy management system. Short-term load forecasting not only provides guarantee for the safe and economical operation of the power system, but also is the basis for scheduling dispatching plans, power supply plans, and trading plans in the market environment. [0003] Short-term load forecasting usually refers to forecasting within one year in the future, load forecasting in units of months, weeks, days, and hours, including load forecasting for 24 hours in the next day. Short-term load plays a vital role in the daily dispatch of power systems. It can provide important data basis for system security analysis, computer online control of power grids and basic power generation plans, and effectively promote ...

AI Technical Summary

Problems solved by technology

The disadvantage is that the law of load change is required to have an exponential change trend. When the degree of data dispersion is greater, that is, the greater the gray level of the data, the worse the prediction accuracy is.

The disadvantage of the expert system is that it is prone to human error in the forecasting process; it is difficult to express and transform expert knowledge and experience into a series of rules, so it is difficult to construct a database; characteristics, the developed expert system is aimed at a specific system and cannot be directly applied to other systems

The disadvantage of the SVM method is that due to large storage requirements, programming difficulties, and practical application are difficult, and it is impossible to determine whether the knowledge in the data is redundant or not, and the magnitude of the effect; for a system with a smoother predicted load curve, it can obtain a more ideal However, for small and medium-sized power grids with strong random fluctuations, the prediction effect is relatively poor

Images