DISTRICT HEATING & COOLING

Improved Asset Reliability and Availability
To Ensure a Stable Supply of Heating, Cooling, and Electricity

0
tons/year reduction in emissions at a site
0%
analysis of key performance energy indicators
0%
reduction in data management hours

WHEN YOUR SYSTEM FAILS, AN ENTIRE COMMUNITY IS AFFECTED

OUR SOLUTIONS PROVIDE ACTIONABLE INTELLIGENCE

TO MAINTAIN RELIABLE OPERATIONS

VALUE WE PROVIDE

CENTRALIZED DATA ANALYSIS

Analyze historical and real-time data throughout the entire organization at any time and anywhere.

IMPROVED ENERGY MANAGEMENT

Improve your energy management to reduce energy usage and emissions with an intuitive dashboard.

ENHANCED REPORTING

Share insights and trends through built-in visualizations and reporting tools to improve data collaboration.

REPORTING

Purpose: Identify buildings with energy usage increases or decreases

Raw Data

  1. Electrical meter values
  2. Chilled water meter values
  3. Steam meter values
  4. Meter check

Summary Review

  1. Top 10 buildings with usage overages
  2. Cost over expected

Purpose: evaluate performance of chilling stations monthly

Raw Data

  1. Evaporator Water Flow
  2. Entering Chilled Water Temp
  3. Leaving Chilled Water Temp
  4. Calculated Chiller Output
  5. Delta T
  6. Chiller kW Usage

Monthly Review

  1. Output (Total Ton Hrs for Day)
  2. Power Use (Total Power Con Day in kWh)
  3. Avg kW/Ton

Purpose: monitor gas budget monthly (actual vs budgeted)

Raw Data

  1. Gas Flow

Monthly Review

  1. Last FY Actual Gas Use, mmBtu
  2. Gas Use Budget, mmBtu
  3. Base Load, mmBtu
  4. Actual from Meters, mmBtu
  5. Locked and Swing Volumes (Costs)

Purpose: monitor overall power plant (monthly and annually)

Monthly Review: Electrical Production

  1. Total Generated MWH
  2. Total System MWH
  3. In-Plant MWH
  4. In-Plant PCT
  5. Total System MWH from System Power

Monthly Report: Steam Production

  1. 425 PSI Steam Produced
  2. In-Plant Use
  3. In-Plant PCT

Monthly Report: Energy to Distribution

  1. Total MWH to Distribution
  2. Total Steam to Distribution, Lbs

Monthly Review: Performance Measures

  1. Total Plant Output mmbtu
  2. Total Plant Input mmbtu
  3. Plant Thermal Efficiency
  4. Plant Heat Rate
  5. HDD Base 65
  6. CDD Base 50

Purpose: monitor chilled water production and system and compare by month and year

Raw Data

  1. Chiller Tons Output
  2. Chiller Evaporator Flow
  3. Chiller Evaporator Water Inlet Temp
  4. Chiller Evaporator Water Outlet Temp
  5. Chiller Station Output
  6. Chiller Mixed Return Temperature
  7. Chiller Return Tunnel Temperature

Summary Review

  1. Total System
    1. Peak Output (Tons)
    2. Average Output (Tons)
    3. Min Output (Tons)
  2. System Delta T
    1. Peak Delta T
    2. Average Delta T
    3. Min Delta T

KPI MANAGEMENT

Purpose: track continuously key indicators and performance metrics across the organization

Indicators

  • Energy to campus (mmbtu), electricity, heating, cooling)
  • Power to chilling stations
  • Natural gas use
  • Overall average monthly total efficiency (total sought over bought)
  • Electricity, heating, cooling/total fuel in
  • Average annual total efficiency
  • Heat Rate
  • Delta T
  • KW/ton
  • Reliability
  • Safety
  • Condensate return % and quality
  • Parasitic loads in plants
  • Chilled water DP (supply vs distribution)
  • HDD, CDD
  • Water and wastewater consumption

SOLUTIONS WE PROVIDE

HanPrism

Centralized data historian and data management platform

Learn More

Dashboard

Customizable and Centralized Dashboard and Reporting Tool

Learn More

HanPHI

Advanced pattern recognition and predictive maintenance solution

Learn More

DISTRICT ENERGY STORY

ACTIONABLE INTELLIGENCE

THE UNIVERSITY OF TEXAS AUSTIN

To reduce unexpected equipment failures, unplanned maintenance activities, and outage time, UT Austin chose HanAra Software’s HanPHI solution as its condition-based predictive monitoring solution.

With the implementation of HanPHI, HanAra provided the UT Austin team with the information to simplify and speed-up the decision-making and maintenance process. With a centralized location for data and the capacity to utilize predictive maintenance, HanPHI also eliminates redundant and unnecessary maintenance and support costs.

With the implementation of HanPHI, we now have usable data. Without manual calculations and long hours, we have the ability for deep insight into all of our systems, which allows us to optimize our efficiency.

Juan Ontiveros, Utilities and Energy Management

Ready to break through the limitations?

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