ICONICS > 实例探究 > University transforms energy management with big data solution

University transforms energy management with big data solution

公司规模

1,000+

地区

America

国家

United States

产品

ICONICS FDD solution
Microsoft SQL Server

技术栈

Big Data
Internet of Things
SQL Server

实施规模

Enterprise-wide Deployment

影响指标

Cost Savings
Environmental Impact Reduction
Productivity Improvements

技术

分析与建模 - 大数据分析
分析与建模 - 实时分析
传感器 - 环境传感器

适用行业

教育

适用功能

设施管理
维护

用例

建筑能源管理
预测性维护
实时定位系统 (RTLS)

服务

软件设计与工程服务
系统集成

关于客户

The University of Nebraska-Lincoln (UNL) is a public research university based in Lincoln, Nebraska. It is the state’s oldest university and currently serves more than 25,000 students. UNL is a strong proponent of environmental sustainability and has set ambitious goals to reduce energy use across its buildings. The university has a Building Systems Maintenance (BSM) team that ensures the comfort of students, faculty, and staff by maintaining optimal building temperatures, functioning alarm systems, and lighting. The BSM team is also responsible for installing energy-efficient equipment and automating building systems to minimize energy waste. The university has over 16.5 million gross square feet of building space, making energy savings a significant focus for its resource management efforts.

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挑战

The University of Nebraska-Lincoln (UNL) had set an ambitious goal of reducing energy use by 15 percent across many of its buildings. To achieve this, the university had been installing energy-efficient equipment and automating building systems to ensure that mechanical and electrical equipment minimized energy waste. However, just like a brand-new car that’s driven off the lot, building equipment starts to deteriorate as soon as it’s installed, losing energy efficiency over time. To mitigate deterioration, UNL had in the past performed ad-hoc recommissioning, but was now developing a comprehensive recommissioning program. The university wanted to find a way to maintain optimal performance without the expense of periodic recommissioning.

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解决方案

To reduce energy consumption and maintain optimal performance of building equipment, UNL implemented a fault detection and diagnostics (FDD) solution developed by Microsoft CityNext partner ICONICS. The solution combines big data with the Internet of Things (IoT) to help UNL identify and fix problems well before mechanical equipment breaks down. The university has attached sensors to equipment in buildings across its campus to monitor performance in real time. When a sensor captures a reading outside of designated parameters set by the BSM team, an alert is triggered and sent to the university’s maintenance control center, where the issue is either fixed remotely or a technician is dispatched to the building. UNL began installing the ICONICS FDD solution in early 2016 and currently uses it to monitor 52 buildings. In the next few years, the university plans to track as many as 80 major university buildings with FDD, along with five campus utility plants.

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运营影响

UNL is avoiding catastrophic breakdowns, while maintaining energy efficiency gains. Rather than recommissioning campus buildings every five years and watching energy efficiency diminish over time, the BSM team now continuously commissions mechanical systems—making small adjustments to preserve building equipment performance.
By resolving issues as soon as they occur, the university is also cutting energy costs.
The BSM team has also transformed how it operates. When an alert is triggered, engineers often fix the issue remotely, at little to no cost. If technicians need to physically travel to the building, they have detailed information about the problem before they arrive, allowing them to repair issues faster.
By addressing issues before they become critical, BSM helps maintain a more comfortable learning atmosphere for UNL students and faculty, which also improves relations.

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数量效益

It’s possible to avoid energy costs of as much as $25,000 per air handler each year—a savings of 84 percent.
In the first year of implementation, the solution detected about 1,100 faults on average each month. Left uncorrected for a year, these would have added up to almost $200,000 in wasted energy.

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