Abstract
The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.
Highlights
With the development of smart grid technology, massive deployment of the advanced metering infrastructure (AMI) and all kinds of monitoring systems generate and accumulate a large amount of data [1]
It is important to mine the values of these data fully; for example, the user electricity behaviors can be mined via smart meter data and the accuracy of load forecasting can be improved according to user electricity behaviors
This study shows that deep mining of the smart meter user electricity behaviors helps to improve the accuracy of load forecasting; The date type is usually introduced while making load forecasting, the date type should be considered regarding clustering loads
Summary
With the development of smart grid technology, massive deployment of the advanced metering infrastructure (AMI) and all kinds of monitoring systems generate and accumulate a large amount of data [1]. This study shows that deep mining of the smart meter user electricity behaviors helps to improve the accuracy of load forecasting; The date type is usually introduced while making load forecasting, the date type should be considered regarding clustering loads. Clustering does not calculate typical day load curves respectively according to different date types; the analysis of user electricity behavior is not accurate enough and the data used is not smart meter data, which affects the accuracy of load forecasting [25,26]. The chosen typical day load curves in clustering do not distinguish date type and average day load; typical day load curves are not calculated according to different date types, which causes the analysis of user electricity behaviors to be not accurate enough and affects the accuracy of load forecasting. In order to prove the validity of the proposed method, we perform simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation
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