Friday 28 March 2025
A team of researchers has developed a comprehensive model predictive control strategy for optimizing chiller plant performance in commercial buildings, which could lead to significant energy savings and reduced environmental impact.
The study’s findings highlight the importance of accurate cooling load prediction in achieving optimal chiller plant operation. Cooling loads are typically predicted using weather data, but this approach can be prone to errors due to uncertainties in weather forecasting. To overcome this challenge, the researchers employed a neural network with Kalman filtering and K-means clustering to predict cooling loads.
The proposed model integrates real-time monitoring data from various building systems, including weather data, to generate accurate predictions of cooling loads. This information is then used to optimize chiller plant operation, ensuring that energy consumption is minimized while maintaining comfortable indoor temperatures.
The researchers tested their strategy on a commercial skyscraper in Singapore’s central business district and found that it improved cooling load prediction accuracy by 46.5%. Additionally, an optimal chiller sequencing strategy was developed for the studied building, resulting in potential energy savings of 13.8%.
The study also explored the integration of thermal energy storage systems into the chiller plant design. Thermal energy storage can help reduce peak demand during periods of high cooling load and decrease energy consumption by storing excess heat or cold during off-peak hours.
The researchers suggest that their strategy could be applied to various commercial buildings, particularly those with centralized air-conditioning systems. By optimizing chiller plant operation, building owners and managers can reduce energy consumption, lower operating costs, and minimize environmental impact.
The study’s findings are significant because they demonstrate the potential for data-driven approaches to improve building energy efficiency and sustainability. As cities around the world grapple with the challenges of climate change and urbanization, innovative solutions like this one could play a crucial role in reducing our carbon footprint and creating more sustainable environments.
Cite this article: “Optimizing Chiller Plant Performance for Energy Efficiency and Sustainability in Commercial Buildings”, The Science Archive, 2025.
Chiller Plant Performance, Predictive Control, Commercial Buildings, Energy Savings, Environmental Impact, Cooling Load Prediction, Neural Network, Kalman Filtering, K-Means Clustering, Thermal Energy Storage.
