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Hitachi

Hitachi High-Tech in America

PACE AI Suite

The PACE AI Suite consists of a rugged, UL-listed industrial IoT edge node, with cloud-based AI/ML to both optimize and monitor Heating Ventilation Air Conditioning and Refrigeration (HVACR) equipment. It can be used for both building and industrial process heating/cooling applications. Each installation delivers remote dashboarding from any mobile device, with easy-to-use cloud API for interoperability with a wide range of building and process systems. PACE AI Suite's proven, patented AI/ML technology has reliably delivered 20-25% or more energy cost savings for a wide range of cooling, heating, and refrigeration equipment, in various climate zones in North America/Caribbean.

Overview

The PACE AI Suite is breakthrough edge+cloud technology to deliver connectivity, 24/7 monitoring, and predictive maintenance, and optimization for the equipment that heats and cools the world. The patented PACE AI/ML technology has reliably delivered 20-25% or more energy cost savings for a very wide range of cooling, heating, and refrigeration equipment, proven in various climate zones in North America and the Caribbean. The PACE AI Suite is qualified for many energy-efficiency incentives and rebates across North America, which may cover 50% and more of installed cost, and is qualified as well for utility On-Bill Funding programs, which are zero-upfront-cost ways for commercial electric- and gas-utility clients to pay for energy-saving projects.

Challenges Addressed

  • HVACR units require maintenance, typically operate stand-alone, and offer operators limited visibility to status
  • Traditional energy-efficiency approaches require equipment replacement, or costly equipment installations which extend paybacks

Solution

PACE AI Suite HVACR monitoring node + cloud-base AI/ML optimization

  • Edge+cloud solution utilizes AI/ML to optimize response of compressors/burners/fans to demand, adding efficiency
  • AI/ML works by enhancing heat transfer, and by reducing control hysteresis and making better use of thermal mass in the HVACR heat exchangers to maintain cooling (or heating) setpoints, with better efficiency

Benefits

  • Breakthrough, industry-leading performance and economics—typically 1- to 3-year paybacks across the U.S., before incentives where available
  • Patented PACE AI machine-learning technology typically reduces electric and fossil fuel energy use by 20-25% or more, with no sacrifice to end-use performance
  • Easy-to-install smart grid retrofit solution—30-40 minutes by any HVAC technician
  • Savings from your existing equipment, plug-and-play, complementary with existing controls
  • Software- and cloud-based machine protections to extend the useful life on existing equipment
  • Equipment fault monitoring and reporting functions via dashboard
  • ENERGY STAR Partner—excellent for a wide range of commercial and industrial applications

ENERGY STAR

How it works

In Cooling and Refrigeration Systems

In vapor-compression cooling and refrigeration, which is the vast majority of equipment worldwide, cooling and dehumidification are both driven by heat transfer through the heat exchanger (evaporator), and the vaporization of the refrigerant as it passes through the evaporator. PACE AI/ML optimizes compressor/fan/pump operation to enhance machine response to each control input, either from a thermostatic input or a building or process automation system. The result: improved energy efficiency and average demand reduction, plus component benefits, without compromising performance.

<Office cooling unit energy consumption vs. outside temperature, without (blue) and with (red) PACE AI online>

Office cooling unit energy consumption vs. outside temperature, without (blue) and with (red) PACE AI online

In Heating Systems

Similarly to cooling and refrigeration systems, energy is consumed in the transfer of thermal energy across the heat exchanger from its production by gas- or oil-fired burners or by electric resistance elements. PACE AI/ML technology is able to learn the call and response of the thermostatic input, optimizing burner and heating element operation to more closely match the demand in real time. The result: improved energy efficiency and fossil fuel savings, plus component benefits, without compromising performance.

<Retail heating unit energy consumption vs. outside temperature, without (blue) and with (red) PACE AI online>

Retail heating unit energy consumption vs. outside temperature, without (blue) and with (red) PACE AI online

Solution Architecture

Solution Architecture

Applications

  • Package and split-system air-conditioning/heat units
  • Heat pumps (air-, ground-, and water-source)
  • Chiller units (non-centrifugal)
  • Electric resistance heating/preheating
  • Boilers and furnaces (natural gas, fuel oil)
  • Hot water heaters (electric, natural gas, propane)
  • Walk-in coolers and freezers
  • Refrigerated warehouses
  • Selected process heating and cooling equipment

Photo of smartphone-sized PACE Node in New York City rooftop HVAC unit: installs in 30-45 minutes, 25%+ energy savings plus real-time monitoring, fault detection and predictive maintenance

Case Studies

Major Quick Serve Restaurant

Challenges

According to U.S. Energy Information Administration (EIA) Commercial Buildings Energy Consumption Survey in 2012*, food service, inpatient health care and food sales buildings are the top 3 energy users (see Figure 1 below). Food-service building consume large amounts of energy primarily for cooking and refrigeration, which consist of 40% of total energy consumption (64% including heating and cooling).

Figure 1: EIA survey: Total Energy Use per Square Foot
Figure 1: EIA survey: Total Energy Use per Square Foot

It is a significant challenge for QSRs to reduce energy consumption while they maintain food-safe temperatures in refrigeration unit to meet Food Safety Modernization Act (FSMA) safety requirement (click here to learn more about FSMA).

Solutions

  1. Pace Nodes were installed in major quick serve chain restaurant location in Philadelphia. The restaurant had four rooftop package units (RTUs) with gas-fired heating.
  2. (2) Pace AI kits were installed on each RTU; one for the cooling unit and the other for heating. Gas and electric energy-consumption information obtained from utility bills for pre- and post-retrofit was normalized based on degree days obtained from a nearby airport, Northeast Philadelphia Airport, and NOAA (https://www.noaa.gov/).

Pre-vs. post-retrofit saving results

<Cooling>
The post-retrofit kWh reduction was determined on a winter-baselined, degree-day-normalized basis for months (June-September) and compared to the same period during the previous year. The kWh overall savings were calculated at 9% (Figure 2). Figure 3 presents the normalized monthly restaurant kWh consumption for the summer months year over year, pre- vs. post-retrofit installation.

Figure 2: Pre-vs. post-retrofit kWh consumption comparison
Figure 2: Pre-vs. post-retrofit kWh consumption comparison

Figure 3: The normalized monthly restaurant kWh consumption comparison
Figure 3: The normalized monthly restaurant kWh consumption comparison

<Heating>
For the heading season, the gas reduction (CCFs) using a similar summer-baselined, degree-day-normalized procedure during a year-over-year comparison, was calculated at 16%. Figure 4 below shows a before- and after-comparison example.

Figure 4: Before- and after-comparison example
Figure 4: Before- and after-comparison example

*
https://www.eia.gov/consumption/commercial/reports/2012/energyusage/index.php

Specifications

  • Breakthrough, industry-leading performance and economics—typically 1- to 3-year paybacks across the US, rebate eligible
  • Patented PACE AI machine learning technology reduces electric and fossil fuel energy use and cost—typically by 20%+
  • Savings from your existing equipment, plug-and-play, complementing existing controls
  • Extraordinary credentialing—proven in "gold standard" utility and DOE-funded, IPMVP-compliant testing, over 90 studies
  • Cooling applications from residential to large commercial (3 to 100+ tons)
  • Heating applications from residential to large boilers (80K BTU/hr and lager)
  • Refrigeration applications from light commercial (walk-ins) to refrigerated warehouses
  • Short cycle protection, starts per hour protection & staggered start for multi-unit installations
  • Simple universal control circuitry, with installation by any HVACR technician
  • Requires no additional sensors—however, accepts a wide range of optional IoT sensors for enhanced performance and/or extreme environmental conditions
  • Fail safe operation—reverts to original thermostat control
  • Over 20,000 installations, huge range of building types and climate zones
  • ENERGY STAR Partner—perfect for FEMP programs