Clustering electricity market participants via FRM models

Collateral mechanism in the Electricity Market ensures payments are executed on a timely manner; thus maintains continuous cash flow. In order to value collaterals, Takasbank, authorized central settlement bank, creates segments of market participants by considering their short-term and long-term debt/credit information arising from all activities. this study, data regarding participants’ daily monthly debt payment penalty behaviors is analyzed with aim discovering high-risk that fail clear debts on-time frequently. Different clustering techniques along different distance metrics considered obtain best clustering. Moreover, preprocessing Recency, Frequency, Monetary Value (RFM) scoring have been used determine representation data. The results show Agglomerative Clustering cosine achieves separated when non-normalized dataset used; also acknowledged domain expert.