CASE STUDY
However, managing this complex energy mix has been a significant challenge. The university relies on a combination of natural gas, biomass and wind, requiring its facilities team to make critical hour-by-hour decisions on whether to generate power on site or purchase electricity from the market.
These decisions are further complicated by volatile fuel prices and fluctuating wholesale electricity costs set by the Midcontinent Independent System Operator. Market prices are only announced the day before, creating a reactive and labour-intensive environment that made it difficult to optimise for cost effectiveness and system resiliency in real time.
The partnership has delivered significant operational and business value, allowing the University of Missouri to move beyond a reactive energy management strategy.
To address these challenges, the University of Missouri partnered with Radix, a technology firm specialising in energy optimisation for large institutions. Radix assembled a multidisciplinary team of engineers and data scientists to develop a customised system designed to give the university’ s facilities team the tools needed for proactive decision-making.
The resulting solution is an innovative platform that uses data science and machine learning to deliver a holistic view of campus energy operations. It integrates operational data with external inputs to support smarter and faster decisions across the entire utility ecosystem. impacting operational costs and sustainability efforts. Large campuses are major energy consumers and must balance the need for cost-effective utility spending with the growing complexity of resiliency projects required to support future expansion, both in design and construction.
As the first public university west of the Mississippi, the University of Missouri operates a campus spanning 17 million square feet. Its extensive Combined Cooling Heating and Power facilities play a central role in campus operations, with a capacity for 60 MW of electricity, 1.1 million lbs per hour of steam, 34,000 tons of chilled water production and four million gallons per day of potable water.
Supporting these operations is an underground distribution network extending 123 miles.
This infrastructure underpins an annual utility energy usage valued between US $ 18m and US $ 20m, highlighting both the scale of operations and the importance of optimising performance across the system.
Key components of the system include plant performance modelling, which uses mathematical models of critical assets to simulate operational performance under varying conditions. Demand forecasting provides accurate hour-by-hour predictions for power, steam and chilled water requirements, allowing operators to anticipate changes rather than respond after the fact.
Data fusion plays a central role, enabling real-time integration of plant data with weather forecasts and energy market information. This unified data set feeds into a smart advisory function that recommends the most costeffective plant configurations at any given time.
The platform also features intuitive and user-friendly visualisation tools. Clear, action-oriented dashboards present forecasts and recommendations in a way that reduces complexity and supports confident operational decisions without the need for extensive manual analysis.
The partnership has delivered significant operational and business value, allowing the www. intelligentcio. com
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