Indoor fish farms are on the rise, but one of the main challenges for their wider adoption is their high energy consumption. Designing them efficiently can save money and be more sustainable, but choosing the right variables during the initial planning stage is difficult. Experts rely on experience, which can lead to missed optimization opportunities.
A team of scientists from the University of Seoul, Michigan State University, and the Electronic and Telecommunication Research Institute developed an initial design tool using a Building Performance Simulation (BPS) model for indoor fish farms, which was validated with on-site measurement data over 120 days.
New Design Tool
This study presents a novel early design tool that utilizes Building Performance Simulation (BPS) to predict energy consumption in indoor fish farms during the planning stage.
The new tool enables owners and operators to make informed decisions about design variables such as size, equipment, and Recirculating Aquaculture Systems (RAS), resulting in significant energy savings.
Key Features of the Tool
According to the scientists, the following are the main key features of the new tool:
- Data-driven: Unlike traditional methods, the tool is based on real on-site data, providing accurate predictions of energy consumption and operating costs.
- User-friendly: The tool is designed to be user-friendly, allowing users to input key design parameters and receive immediate feedback on their impact on energy performance.
- Comprehensive: The tool considers various factors including environmental systems, equipment types, and local climate to provide a holistic picture of energy usage.
How It Works
The scientists also described the main steps considered for using the new tool, including:
- Model construction: The tool uses EnergyPlus, a popular BPS tool, to create a virtual fish farm based on real-world data. This model can analyze different design options such as building size, equipment types, and even fish species.
- Sensitivity analysis: The tool identifies key design factors that most impact energy consumption. This helps designers focus their efforts on areas with the highest potential for savings.
- Performance prediction: The tool estimates energy usage and operating costs for various design scenarios, enabling optimization and informed decision-making.
Benefits for Fish Farmers
“The average values of MBE and Cv(RMSE) of the BPS model met the error rate within the hourly criteria ranges for the entire measurement period,” report the scientists. In this regard, the new tool will enable fish farmers to:
- Reduce energy consumption: The tool helps design energy-efficient fish farms, minimizing environmental impact and operating costs.
- Informed decision-making: Owners and operators can make data-driven decisions about design variables, leading to optimal performance.
- Early intervention: Addressing energy efficiency at an early stage of the design phase saves time and resources compared to retrofitting existing facilities.
- Flexibility: The tool can be tailored to the needs of different fish species, farm sizes, and locations.
Conclusion
This study represents a significant step towards more efficient and sustainable indoor fish farming. By providing designers with predictive tools, we can build smarter fish farms that benefit both businesses and the environment.
Finally, the scientists also describe the study’s limitations and recommend attention to the following issues:
- Expanding the tool to include more diverse types of fish farms and operational parameters.
- Integrating the tool with optimization algorithms for even better design recommendations.
- Promoting wider adoption of the tool within the aquaculture industry.
The study was executed by the Institute of Information & Communications Technology Planning & Evaluation (IITP) with funding from the Government of Korea and the National Research Foundation of Korea (NRF).
Reference (open access)
Goo, J., Kwak, Y., Kim, J., Kang, J., Shin, H., Jo, S. K., & Huh, J. H. (2024). Development of early design tool for aquaculture buildings using building performance simulation: A case study of an indoor fish farm. Developments in the Built Environment, 100363.
