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Abstract
Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation’s energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data.
Highlights
In order to achieve sustainable green development, the strategic policy of energy conservation and emission reduction [1] has become urgent for China
Förderer et al [6] presented an analysis of options for integrating automated (Building) Energy Management Systems (EMSs) into the smart meter architecture based on the technical guidelines for SMGWs by the German Federal Office for Information Security
In order to further verify the effectiveness of the proposed data recovery algorithm, comparisons with other two kinds of fitting algorithms are conducted and analyzed on a data set obtained from a power metering center
Summary
In order to achieve sustainable green development, the strategic policy of energy conservation and emission reduction [1] has become urgent for China. The data reuses are improved, and a data reordering technique to sort sparse matrices according to nonzeros is proposed Besides those modified alternative least-squares algorithms, several item-based collaborative filtering (CF ) recommendation algorithms are put forward to improve the algorithm accuracy. In order to solve the recovery problem of missed electricity metering data, a novel data recovery algorithm based on collaborative fitting is proposed in this paper. This algorithm can effectively reduce the algorithm complexity and improve the data recovery accuracy. Aiming at the problem of missing data recovery, an improved alternate least-squares matrix factorization method is proposed, and the collaborative fitting method is used to improve the accuracy of data recovery; (2).
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