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Smart meter data classification using optimized random forest algorithm
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DOI：10.1016/j.isatra.2021.07.051.png)
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Abstract
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Implementing a proper clustering algorithm and a high accuracy classifier for applying on electricity smart meter data is the first stage in analyzing and managing electricity consumption. In this paper, Random Forest (RF) classifier optimized by Artificial Bee Colony (ABC) which is called Artificial Bee Colony-based Random Forest (ABC-RF) is proposed. Also, in order to determine the representative load curves, the Convex Clustering (CC) is used. The solution paths generated by convex clustering show relationships among clusters that were hidden by static methods such as k-means clustering. To validate the proposed method, a case study that includes a real dataset of residential smart meters is implemented. The results evidence that the proposed ABC-RF method provides a higher accuracy if compared to other classification methods. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords:
Smart meter
Convex clustering
RF classification
ABC algorithm
Residential customer
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