| Abstrakt: |
Energy-water efficiency forecasting is important to ensure a reliable supply of water for future generations as well as reduce energy consumption. Uncertainty, fuzziness, and hesitation in the information about energy consumption and water production might affect the accuracy of predictions and the effectiveness of models. Forecasting inaccuracy and model efficiency may both be improved by using Intuitionistic Fuzzy Sets, which are able to deal with the uncertainty, fuzziness, and hesitancy inherent in using past data. However, there is no standard technique in intuitionistic fuzzy time forecasting that can handle uncertain data and be utilized to create forecasts that are significantly more accurate than classical time series forecasting. In this paper, the triangular membership function and the trapezoidal membership function of the Intuitionistic Fuzzy Set, which is used to manage the water industry, were compared. This study looks at which of these two shapes of membership functions can make more accurate predictions. This becomes a core procedure in transforming the crisp value into fuzzy values by recognizing uncertainties or variations in the crisp values, and the process is known as fuzzification. Three forecasting errors were used in order to justify the best shape, namely Absolute Percentage Forecasting Error (APFE), Mean Squared Forecasting Error (MSFE), and Mean Absolute Percentage Forecasting Error (MAPFE). The result reveals that the triangular membership function is more accurate compared to the trapezoidal membership function for imprecise data representation in intuitionistic fuzzy time series forecasting. [ABSTRACT FROM AUTHOR] |
