Automating the Measurement & Verification of Energy Efficiency
公司规模
Large Corporate
地区
- Pacific
国家
- United States
产品
- DeltaMeter
- Verdigris
- eQUEST
技术栈
- Cloud Computing
- Machine Learning
- Big Data Analytics
实施规模
- Pilot projects
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 大数据分析
- 分析与建模 - 机器学习
- 功能应用 - 远程监控系统
适用行业
- 建筑物
- 公用事业
适用功能
- 设施管理
- 维护
用例
- 建筑能源管理
- 能源管理系统
- 预测性维护
服务
- 系统集成
- 软件设计与工程服务
关于客户
The case study focuses on building owners in the Pacific Northwest who are increasingly prioritizing energy efficiency due to rising energy costs and a competitive rental market. These building owners face challenges in tracking the return on investment (ROI) from energy efficiency upgrades due to the dynamic nature of buildings. Factors such as shifting weather patterns, changes in occupancy, and varying equipment lifespans complicate the isolation of savings from energy efficiency investments. Traditional methods of verifying savings, such as hiring energy engineers to create predictive thermodynamic energy models, are often complicated and expensive. The case study discusses two low-cost methods available to building owners to improve the tracking of ROI from energy efficiency efforts.
挑战
Building owners and property managers face significant challenges in verifying the return on investment (ROI) from energy efficiency measures due to the dynamic nature of buildings. Factors such as shifting weather patterns, changes in occupancy, and varying equipment lifespans complicate the isolation of savings from energy efficiency investments. Traditional methods of verifying savings, such as hiring energy engineers to create predictive thermodynamic energy models, are often complicated and expensive. These methods involve intensive data collection and calibration processes, which can cost between $0.10 to $0.50 per square foot, making them cost-prohibitive for many building owners. Additionally, traditional methods have limited capability for tracking ongoing performance, which is crucial for identifying failures or below-average performance of energy efficiency measures.
解决方案
The case study explores two low-cost automated measurement and verification (M&V) technologies: DeltaMeter and Verdigris. DeltaMeter, developed by EnergyRM, is a software platform that uses patented algorithms and monthly energy bills to create a thermodynamic model at the end-use level. This model estimates the baseline energy use and tracks the actual building performance to reveal the savings achieved. DeltaMeter does not require physical deployment at the building site and offers diagnostic analysis, savings potential recommendations, and ongoing M&V crosschecks. Verdigris, developed by a Northern California-based company, uses advanced analytics and in-house M&V technology to analyze high-resolution electricity consumption data from building circuits. Verdigris employs custom machine learning algorithms to predict a baseline and disaggregate energy by end use. The platform provides real-time data streams, energy efficiency recommendations, peak demand analysis, and equipment-level monitoring. Both technologies were deployed in a pilot project at eight high-profile commercial buildings in Seattle, commissioned by the Smart Buildings Center (SBC) in partnership with the Northwest Energy Efficiency Alliance (NEEA). The pilot aimed to evaluate the accuracy, ease of installation, scalability, and training time for operations staff.
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