How Can Factories Save On Energy Costs and Improve Energy Efficiency?

By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.Dec 4 2024

A recent study published in Buildings tackles the critical challenge of accurately assessing the energy performance of industrial factories, a topic often overlooked due to limited data availability and confidentiality concerns.

By conducting a comprehensive investigation of a live factory in South Korea, the researchers were able to develop tailored energy simulation profiles using extended operational data and experimental measurements, offering valuable insights into how energy efficiency in industrial settings can be optimized.

Background

South Korea is working toward an ambitious goal: reducing carbon emissions in the building sector by 32.8 % by 2030 compared to 2018 levels. To get there, efforts like retrofitting and energy remodeling of existing buildings are already underway.

Factory buildings present a unique challenge due to their massive size, high occupancy levels, and substantial energy demands. Unlike commercial buildings, factories have not really been studied much in terms of energy efficiency. This is partly because detailed operational data is often kept confidential for security reasons.

Despite these challenges, stricter regulations on greenhouse gas emissions and growing sustainability commitments are driving the need to improve energy efficiency in factories. While simulations have been used to explore ways to optimize energy use in industrial settings, the role of specific building features in energy performance has not been fully understood—until now.

Methods

This study focused on a manufacturing factory in South Korea, where researchers conducted a year-long investigation to collect detailed operational data and field measurements. The data was used to create a comprehensive building profile, which was evaluated using South Korea’s building energy performance assessment program, ECO2.

To build an accurate energy simulation, the study analyzed several key variables:

• Air Change Rate per Hour (ACH): Measured through experiments.
• Zone Air Temperature Profiles: Captured through long-term monitoring.
• Internal Heat Gain, Operation Schedule, and Hot Water Demand Profiles: Compiled from operational data.

A total of 7483 data points were recorded for the heating season and 17,450 for the cooling season. MATLAB was used to preprocess this data, removing noise and ensuring accuracy. To enhance the model further, long-term temperature distribution measurements—both vertical and horizontal—were taken to characterize the thermal behavior of the factory’s zones.

Once the building profile was established, the researchers validated its accuracy by comparing energy performance simulations with actual consumption data, using South Korea’s Building Energy Efficiency Rating System as a benchmark.

The study also compared the factory’s unique energy profile with a standard office building profile to identify the distinct energy simulation requirements of industrial facilities. Finally, the reliability of the developed profiles was confirmed by matching the factory’s actual monthly energy consumption with the simulation results.

Results and Discussion

The study revealed that the ACH and temperature setpoints in the factory were similar to those observed in office buildings. While a higher ACH was initially expected due to the factory’s structural characteristics, its lower surface-to-volume ratio—attributed to the vast open spaces—contributed to this unexpected parity.

The energy simulations, based on the detailed building profiles, effectively assessed the factory’s heating and cooling energy requirements. A comparison between the simulation results and actual data showed a root-mean-squared error of 3.9 kWh/m² for cooling and 7.4 kWh/m² for heating energy consumption.

The determination coefficient (R²) further confirmed the accuracy of the simulations, with values of 98.2 % for cooling and 94.1 % for heating, despite minor discrepancies in March and August. The consistently high R² values (>90 %) validate the simulation’s ability to estimate monthly energy consumption in industrial settings.

When comparing the factory and office building profiles, monthly heating energy consumption was found to be similar. However, cooling energy consumption was notably lower for the office building profile, leading to a 2.81 % decrease in the average R² value for simulations using office data.

Although factory buildings typically have longer operational hours compared to offices, their energy consumption patterns differed due to specific internal dynamics. The factory exhibited 3.29 times more internal heat per unit area than the office, driven by its manufacturing processes. Conversely, the office building required 20.4 times more hot water than the factory, reflecting differences in operational demands.

These findings underscore the importance of tailored building profiles for accurate energy performance simulations, particularly for industrial facilities with unique structural and operational characteristics.

Conclusion

This study provided valuable insights into energy simulation for factory buildings by leveraging empirical operational data and experimental measurements from a manufacturing facility in South Korea. The research addressed the lack of publicly available information for factories, which are often characterized by their large size and high-security requirements.

The developed building profiles, tailored for industrial settings, hold significant value for industry stakeholders seeking to enhance energy efficiency. These profiles offer a foundation for more accurate energy performance assessments, addressing a key gap in the current understanding of factory energy dynamics compared to typical office buildings.

However, the study's focus on a single factory limits the broader applicability of its findings. The methodology needs to be expanded to a wider range of manufacturing facilities, enabling the regular updating of building profiles to reflect diverse industrial operations. This approach could support more comprehensive strategies for improving energy efficiency across the sector.

Journal Reference

Lim, H., Park, G.-H., Kim, S., Kim, Y., & Yu, K.-H. (2024). Investigation of Building Profiles for the Energy Simulation of a Factory Building: A Case Study in South Korea. Buildings, 14(12), 3767. DOI: 10.3390/buildings14123767, https://www.mdpi.com/2075-5309/14/12/3767

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