Abstract
With the availability of Smart Grid, disaggregation, i.e. decomposing a whole electricity signal into its component appliances has gotten more and more attentions. Now the solutions based on the sparse coding, i.e. the supervised learning algorithm that belongs to Non-Intrusive Load Monitoring (NILM) have developed a lot. But the accuracy and efficiency of these solutions are not very high, we propose a new efficient sparse coding-based data-mining (ESCD) scheme in this paper to achieve higher accuracy and efficiency. First, we propose a new clustering algorithm – Probability Based Double Clustering (PDBC) based on Fast Search and Find of Density Peaks Clustering (FSFDP) algorithm, which can cluster the device consumption features fast and efficiently. Second, we propose a feature matching optimization algorithm – Max-Min Pruning Matching (MMPM) algorithm which can make the feature matching process to be real-time. Third, real experiments on a publicly available energy data set REDD [1] demonstrate that our proposed scheme achieves a for energy disaggregation. The average disaggregation accuracy reaches 77% and the disaggregation time for every 20 data is about 10 s.
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Acknowledgment
This work is partially supported by China National Key Research and Development Program No. 2016YFB0800301.
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Wang, D., He, J., Rahim, M.A., Zhang, Z., Zhu, L. (2018). An Efficient Sparse Coding-Based Data-Mining Scheme in Smart Grid. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_10
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DOI: https://doi.org/10.1007/978-981-10-8890-2_10
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