IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment
DOI:
https://doi.org/10.69996/jsihs.2024016Keywords:
Internet of Things (IoT), Sensor Network, Electricity Consumption, Cluster Analysis Algorithm, Behaviour AnalysisAbstract
The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity
consumption behavior by collecting real-time data from various connected devices. Utilizing cluster
analysis algorithms, researchers can effectively segment and analyze consumption patterns within a
network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy
efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for
analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior
Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and
weighted classification, the proposed approach allows for the segmentation of consumers into distinct
clusters based on their consumption patterns. Through the application of CBAWC, the study provides
insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption
levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the
algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better
understand consumption trends and tailor energy management strategies accordingly. Through the
application of CBAWC, consumers are segmented into distinct clusters based on their consumption
patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1
consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming
about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per
month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores
ranging from 0.86 to 1.00 across different user groups.
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