| Summary: | Triangular fuzzy numbers (TFNs) are used to express the weights of criteria and alternatives to account for the ambiguity and uncertainty inherent to subjective evaluations. However, the proposed method can easily be extended to other fuzzy settings depending on the uncertainty facing managers and decision-makers. Triangular fuzzy number (TFN) is a critical component in building fuzzy models such as fuzzy regression and fuzzy autoregressive. Many symmetrical triangular fuzzy numbers have been proposed to improve the scale’s linguistic accuracy. Additionally, Sturges’ rule is a well-known approach to determining criteria or intervals of grouped data. However, some existing TFN methods are challenging despite being considered in building fuzzy regression models. The increase in electricity distribution is caused by the number of customers and the amount of installed capacity factors in Indonesia. The identified factors are uncertainty, inexactness, and random nature. This paper investigates the residential electricity distribution model using fuzzy regression time series. In the beginning step, the integration between conventional TFN and Sturges’ rule was proposed to determine the criteria or scale of linguistic terms. The secondary data was collected from BPS Indonesia from 2000 to 2021. The dependent variable was denoted as electric power distribution <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><msub><mrow><mi>Y</mi></mrow><mrow><mi>R</mi><mi>T</mi></mrow></msub><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>. On the other hand, the number of customers and the amount of installed capacity were grouped as independent variables (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>X</mi></mrow><mrow><mi>P</mi><mi>L</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>X</mi></mrow><mrow><mi>K</mi><mi>T</mi></mrow></msub></mrow></semantics></math></inline-formula>). The results showed that the best forecasting model is an FLR right upper limit without constant. This proposed model also has higher MAPE accuracy at 1.44% compared to classical models. Additionally, the proposed triangular fuzzy number could improve the accuracy of the proposed model significantly. Interestingly, both dependent and independent factors were initially forecasted using a basic time series model, namely exponential smoothing.
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