Energy Audit Integrated with Fuzzy Neural Network Predictive Maintenance for Central Chillers - Budiman R Saragih; Aldi Cahya Muhammad (Program Studi Teknik Komputer, 2025)
Because central chiller systems significantly affect electricity usage in office buildings, predictive maintenance and energy audits would be important to increase efficiency. This research analyzes the data from the thermodynamic audit and the central chillers and the monthly electricity usage to assess the energy performance of the East Jakarta Mayor's Office Building A for the years 2023-2024. Based on the Building A, in 2024, the total estimated electrical consumption will be 2,019,550 kWh. This results in total energy use intensity of 106.9 kWh/m²/year. Based on the estimated data, the HVAC systems use more than half of the total electrical consumption. The simulations show, for the data provided, the energy efficiency measures have a saving potential of approximately 728,847 kWh/year which equals 36.1% on a total consumption of 2,019,550 which would also save 36.2% of 1.26 billion/year. The total energy use intensity would be reduced to 90.5 kWh/m²/year, with the emission reduction of approximately 604.9 tCO₂e/year. Based on the consumed data and the paired t test on the 12 sampled data the results would show, with p value < 0.001, a 97,438 kWh/month average reduction in electrical consumption in the 2023-2024 years, which shows a correlation in the expected data with operational and standard fix measures. The Fuzzy Neural Network is, and can be used with other data to show other measures of predictive maintenance rather than the conventional audit based measures used.