A combination of SOM-based operating time estimation and simplified disaggregation for SME buildings using hourly energy consumption data

Komatsu, Hidenori; Kimura, Osamu

doi:10.1016/j.enbuild.2019.07.036

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…The research [8] compared two clustering methods, K-Means and SOM, where the K-Means clustering method had lower accuracy than the SOM method. This research uses the SOM method with different data [24]; SME data is used in Madura. The algorithm step diagram is shown in the Figure 1.…”

Section: K-means Clusteringmentioning

confidence: 99%

Enhancing Small and Medium Enterprises: A Hybrid Clustering and AHP-TOPSIS Decision Support Framework

Khotimah,

Anamisa,

Kustiyahningsih

et al. 2024

ISI

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The vitality of small and medium enterprises (SMEs) is integral to the economic fortification of nations, necessitating refined enhancement mechanisms from governmental bodies. Distinctive in their developmental trajectory, SMEs present a unique challenge in prioritizing interventions. A Decision Support System (DSS), employing a hybridized methodology of Clustering and Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS), is proposed to facilitate the stratification of SMEs and guide governmental action based on a hierarchical scale of priorities. In this study, K-Means clustering was adopted for the segmentation of SMEs, leveraging its capability to efficiently partition high-dimensional data with minimal error. Subsequently, the TOPSIS method was utilized to rank SMEs within each cluster. However, the critical step of computing criteria weights to ascertain their relative importance was achieved through the AHP method. The latter effectively addresses multivariate considerations encompassing both quantitative and qualitative criteria through pairwise comparison matrices. The research incorporated 11 attributes, encompassing essential characteristics intrinsic to business entities, such as business name, location, operational status, sector, tax identification number, workforce size, average revenue, production costs, operational challenges, credit accessibility, and external financing needs. The clustering process, executed via Self-Organizing Maps (SOM), yielded optimal clusters at 1000 epochs, evidenced by a Davies Bouldin Index (DBI) of 0.74785, translating to an accuracy of 91.2601%. Notably, the SOM's performance in clustering SME data surpassed that of K-Means, as demonstrated by superior results in both Sum of Squared Error (SSE) and DBI metrics, thus showcasing its proficiency in managing data with heterogeneous criteria. The methodology engenders an n-cluster output, from which members are earmarked for priority ranking. AHP-derived weights are calculated, with a Consistency Ratio (CR) exceeding 0 denoting consistency, thereby determining the significance of each SME. TOPSIS calculations then ascribe the final score, delineating the SMEs' standings. This integrated DSS framework presents a robust tool for policymakers, ensuring targeted and efficient allocation of resources towards the advancement of SMEs.

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Section: K-means Clusteringmentioning

confidence: 99%