<![CDATA[Energy consumption in the yarn manufacturing industry is significant due to electricity‑intensive processes and auxiliary systems; energy intensity (kWh per kg of yarn) is a key indicator of efficiency and cost. To accurately evaluate savings from efficiency measures, the study developed a multiple linear regression (MLR) baseline model for a large polyester and synthetic yarn plant in West Java Indonesia. The model used historical 2022 data to relate energy use to production output and operational variables, enabling prediction of energy consumptionin the absence of improvements. Statistical analysis showed the regression was significant. The plant’s energy intensity averaged ~3.4–4.1 kWh/kg, which is moderate compared with European benchmarks. Using the model as an energy performance indicator (EnPI), the 2023 monitoring year data were compared to baseline predictions to verify savings. The comparison revealed that energy‑saving initiatives—such as improved motors and climate control—reduced actual energy use below the baseline, confirming real efficiency gains while accounting for factors like production level and weather. The case study demonstrates that MLR‑based EnPI baselines provide a robust framework for moni-toring and verifying industrial energy savings and benchmarking performance.]]>
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