Fine classification and prediction method and system for resident electric load mode

Abstract

The invention discloses a fine classification and prediction method and system for a resident electrical load mode, and the method comprises the steps of collecting the electrical load data and weather data of a resident; screening the meteorological features based on a Bayesian information criterion, wherein the meteorological features meeting the conditions form a meteorological feature library;performing clustering analysis on the resident power load data to obtain a resident power consumption mode; improving the LSTM network by using a fusion activation function; and based on the improvedLSTM network, predicting the residential electricity loads in different electricity consumption modes. The beneficial effects of the invention are that the classification and prediction method provided by the invention can achieve the more precise classification of the power consumption modes of residents, and obtains a more precise prediction result according to the classification result.

Description

technical field [0001] The invention relates to the technical field of electricity consumption forecasting, in particular to a fine classification and prediction method and system for residential electricity load patterns. Background technique [0002] In recent years, with the continuous expansion of the construction scale of the smart grid and the widespread application of smart meters in the power system, the relevant departments of the power company have accumulated a large amount of electricity consumption data, and the valuable information hidden in the information can be excavated through the corresponding data mining technology. At present, a large number of studies have realized the pattern recognition of the electricity load curve through the method of cluster analysis. Clustering the electricity consumption data of residential users can analyze the electricity consumption habits of different residents, so as to summarize the electricity consumption law and charact...

AI Technical Summary

Problems solved by technology

Before clustering, it is necessary to reduce the dimensionality of the data, so as to reduce the complexity and extract the corresponding features. At present, most of the clustering algorithms of load curves use a single Euclidean distance as the similarity measure, and the Euclidean distance is based on The geometric mean distance is used to measure the similarity between samples, and its disadvantage is that it cannot reflect the similarity of the curve shape and trend; in addition, in terms of feature clustering, the traditional K-means algorithm is usually used, which is more accurate in identifying the peak of power consumption. However, different electricity consumption patterns under the same electricity consumption level cannot be accurately identified, that is, the granularity of clustering is not enough, which will affect the accuracy of classification and prediction results of residential electricity load patterns

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