Adaptive power usage effectiveness for data centers

Abstract

Aspects and embodiments disclosed herein include a method of performing an adaptive calculation of power usage efficiency (PUE) of a data center. The method comprises receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

Description

[0001] ADAPTIVE POWER USAGE EFFECTIVENESS FOR DATA CENTERS

[0002] BACKGROUND

[0003] 1. Field of the Disclosure

[0004] At least one example in accordance with the present disclosure relates generally to systems and methods for determining and displaying power usage effectiveness values for a data center.

[0005] 2. Discussion of Related Art

[0006] Centralized information technology (IT) rooms or data centers for computer, communications, and other electronic equipment contain numerous equipment racks of equipment that require power, cooling, and connections to external communications facilities. Electronic equipment contained in the equipment racks generate substantial heat and accordingly typical equipment racks use air flow through the racks to cool the electronic equipment.

[0007] One metric that users find important for data centers is the power usage effectiveness (PUE) of the data center. PUE is a ratio that indicates how effectively a data center utilizes energy provided to the data center. PUE is a measure of how much energy is utilized by the information technology (IT) equipment (servers, memory, communications, etc.) as compared to overhead that supports operation of the IT equipment, for example, cooling systems for the data center. PUE may be calculated as Total Data Center Energy Usage / IT Equipment Energy Usage. An ideal PUE ratio would be 1.0.

[0008] SUMMARY

[0009] In accordance with one aspect, there is provided a method of performing an adaptive calculation of power usage efficiency (PUE) of a data center. The method comprises receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of values associated with the parameters of the one or more items of equipment in the data center, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

[0010] In some embodiments, the method further comprises presenting the estimated PUE and the first measure of uncertainty to the user.

[0011] In some embodiments, the method further comprises receiving readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

[0012] In some embodiments, determining the estimated PUE and a measure of uncertainty in the estimated PUE includes obtaining power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

[0013] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes prompting a user to identify the quantities and types of equipment in the data center.

[0014] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of power supplies in the data center.

[0015] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of cooling systems in the data center.

[0016] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of lighting systems in the data center.

[0017] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of information technology (IT) systems in the data center.

[0018] In some embodiments, receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

[0019] In some embodiments, receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply. In some embodiments, the method further comprises inferring a cooling system power load from one or more of the load on the power supply, the input power to the power supply, or the output power from the power supply.

[0020] In some embodiments, receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of temperature inside the data center or temperature of an environment external to the data center.

[0021] In some embodiments, refining the estimated PUE and measure of uncertainty in the estimated PUE includes determining the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

[0022] In some embodiments, the method further comprises calculating a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

[0023] In some embodiments, the method further comprises receiving an indication of a location of the data center and utilizing the location as a factor in determining the estimated PUE.

[0024] In accordance with another aspect, there is provided a method of performing an adaptive calculation of power usage efficiency (PUE) of a data center. The method comprises receiving a first input defining quantities and types of equipment in the data center, creating a model of the data center from the first input, determining if the first input omits one or more types of equipment expected to be present in the data center, responsive to determining that the first input omits one or more of the types of equipment expected to be present in the data center, determine a most likely type or types of the one or more types of equipment expected to be present in the data center and omitted from the first input as one or more inferred equipment types, values of operational parameters of the one or more inferred equipment types being modelled as spanning a range encompassing values of operational parameters of all of the most likely type or types of the one or more types of equipment, adding any inferred equipment types to the model of the data center, determining an estimated PUE and a measure of uncertainty in the estimated PUE from the model of the data center, receiving a second input defining quantities and types of equipment in the data center omitted from the first input, responsive to one or more of the types of equipment received in the second input being a functional equivalent to one or more of the inferred equipment types, updating the model of the data center by substituting any inferred equipment types with the functional equivalent equipment types, determining an updated estimated PUE and an updated measure of uncertainty in the updated estimated PUE responsive to updating the model of the data center, and presenting the updated estimated PUE and measure of uncertainty in the updated estimated PUE to a user.

[0025] In some embodiments, the method further comprises receiving readings of operational parameters of one or more items of the equipment in the data center from one or more sensors operatively connected to the one or more items of the equipment, refining one of the estimated PUE and measure of uncertainty in the estimated PUE or the updated estimated PUE and measure of uncertainty in the updated estimated PUE based on the received readings to obtain a refined PUE and measure of uncertainty in the refined PUE, and presenting the refined PUE and measure of uncertainty in the refined PUE to a user.

[0026] In accordance with another aspect, there is provided a system configured to perform an adaptive calculation of power usage efficiency (PUE) of a data center. The system comprises a controller configured to receive a first input defining quantities and types of equipment in the data center, determine an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receive a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refine the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of values associated with the parameters of the one or more items of equipment in the data center, and present the refined PUE and the second measure of uncertainty in the refined PUE to a user.

[0027] In some embodiments, the controller further is configured to present the estimated PUE and the first measure of uncertainty to the user.

[0028] In some embodiments, the controller further is configured to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

[0029] In some embodiments, receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

[0030] In some embodiments, receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

[0031] In some embodiments, the controller is further configured to infer a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

[0032] In some embodiments, receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of temperature inside the data center or temperature of an environment external to the data center.

[0033] In some embodiments, determining the estimated PUE and a measure of uncertainty in the estimated PUE includes obtaining power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

[0034] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes prompting a user to identify the quantities and types of equipment in the data center.

[0035] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of power supplies in the data center.

[0036] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of cooling systems in the data center.

[0037] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of lighting systems in the data center.

[0038] In some embodiments, receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of information technology (IT) systems in the data center.

[0039] In some embodiments, refining the estimated PUE and measure of uncertainty in the estimated PUE includes determining the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

[0040] In some embodiments, the controller further is configured to calculate a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center. In some embodiments, the controller further is configured to receive an indication of a location of the data center and utilizing the location as a factor in determining the estimated PUE.

[0041] In accordance with another aspect, there is provided a non-transitory computer readable medium having instructions encoded therein which when executed by a computer system cause the computer system to perform a method comprising receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of the values associated with the parameters of the one or more items of equipment in the data center, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

[0042] In some embodiments, the instructions further cause the computer system to presenting the estimated PUE and the first measure of uncertainty to the user.

[0043] In some embodiments, the instructions further cause the computer system to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

[0044] In some embodiments, the instructions further cause the computer system to determine the estimated PUE and the measure of uncertainty in the estimated PUE from power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

[0045] In some embodiments, the instructions further cause the computer system to prompt a user to identify the quantities and types of equipment in the data center.

[0046] In some embodiments, the instructions further cause the computer system to prompt a user to identify a quantity and type of power supplies in the data center.

[0047] In some embodiments, the instructions further cause the computer system to prompt a user to identify a quantity and type of cooling systems in the data center.

[0048] In some embodiments, the instructions further cause the computer system to prompt a user to identify a quantity and type of lighting systems in the data center. In some embodiments, the instructions further cause the computer system to prompt a user to identify a quantity and type of information technology (IT) systems in the data center.

[0049] In some embodiments, the instructions further cause the computer system to receive an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

[0050] In some embodiments, the instructions further cause the computer system to receive an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

[0051] In some embodiments, the instructions further cause the computer system to infer a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

[0052] In some embodiments, the instructions further cause the computer system to receive an indication of one of temperature inside the data center or temperature of an environment external to the data center.

[0053] In some embodiments, the instructions further cause the computer system to determine the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

[0054] In some embodiments, the instructions further cause the computer system to determine a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

[0055] In some embodiments, the instructions further cause the computer system to receive an indication of a location of the data center and utilize the location as a factor in determining the estimated PUE.

[0056] In accordance with another aspect, there is provided a non-transitory computer readable medium having instructions encoded therein which when executed by a computer system cause the computer system to perform a method comprising receiving a first input defining quantities and types of equipment in the data center, creating a model of the data center from the first input, determining if the first input omits one or more types of equipment expected to be present in the data center, responsive to determining that the first input omits one or more of the types of equipment expected to be present in the data center, determine a most likely type or types of the one or more types of equipment expected to be present in the data center and omitted from the first input as one or more inferred equipment types, values of operational parameters of the one or more inferred equipment types being modelled as spanning a range encompassing values of operational parameters of all of the most likely type or types of the one or more types of equipment, adding any inferred equipment types to the model of the data center, determining an estimated PUE and a measure of uncertainty in the estimated PUE from the model of the data center, receiving a second input defining quantities and types of equipment in the data center omitted from the first input, responsive to one or more of the types of equipment received in the second input being a functional equivalent to one or more of the inferred equipment types, updating the model of the data center by substituting any inferred equipment types with the functional equivalent equipment types, determining an updated estimated PUE and an updated measure of uncertainty in the updated estimated PUE responsive to updating the model of the data center, and presenting the updated estimated PUE and measure of uncertainty in the updated estimated PUE to a user.

[0057] In some embodiments, the instructions further cause the computer system to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors operatively connected to the one or more items of the equipment, refine one of the estimated PUE and measure of uncertainty in the estimated PUE or the updated estimated PUE and measure of uncertainty in the updated estimated PUE based on the received readings to obtain a refined PUE and measure of uncertainty in the refined PUE, and present the refined PUE and measure of uncertainty in the refined PUE to a user.

[0058] BRIEF DESCRIPTION OF DRAWINGS

[0059] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

[0060] FIG. 1 is a block diagram of one example of a computer system with which various aspects in accord with the present invention may be implemented;

[0061] FIG. 2 is a schematic of one example of a distributed system including an IT room management system;

[0062] FIG. 3 is an example of a dashboard displaying PUE estimates at customer sites; FIG. 4 is an example of dashboard displaying trends in PUE over time for a site; FIG. 5A illustrates one example of a model of a data center;

[0063] FIG. 5B illustrates another example of a model of a data center;

[0064] FIG. 5C illustrates another example of a model of a data center;

[0065] FIG. 5D illustrates another example of a model of a data center; FIG. 5E illustrates another example of a model of a data center;

[0066] FIG. 6 illustrates an example of a strategy for calculating IT load and PU load in a datacenter;

[0067] FIG. 7 illustrates schematically how a difference upper and lower bounds of an estimate of PUE for a data center decreases with increased knowledge about the configuration of the data center;

[0068] FIG. 8 is a chart in which worst / best case curves for a data center PUE calculation are plotted against PU load for the data center; and

[0069] FIG. 9 is a flowchart of a method that may be performed in accordance with the present disclosure.

[0070] DETAILED DESCRIPTION

[0071] Examples of the methods and systems discussed herein are not limited in application to the details of construction and the arrangement of components set forth in the following description or illustrated in the accompanying drawings. The methods and systems are capable of implementation in other embodiments and of being practiced or of being carried out in various ways. Examples of specific implementations are provided herein for illustrative purposes only and are not intended to be limiting. In particular, acts, components, elements, and features discussed in connection with any one or more examples are not intended to be excluded from a similar role in any other examples.

[0072] Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. Any references to examples, embodiments, components, elements or acts of the systems and methods herein referred to in the singular may also embrace embodiments including a plurality, and any references in plural to any embodiment, component, element or act herein may also embrace embodiments including only a singularity. References in the singular or plural form are not intended to limit the presently disclosed systems or methods, their components, acts, or elements. The use herein of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[0073] References to “or” may be construed as inclusive so that any terms described using “or” may indicate any of a single, more than one, and all of the described terms. In addition, in the event of inconsistent usages of terms between this document and documents incorporated herein by reference, the term usage in the incorporated features is supplementary to that of this document; for irreconcilable differences, the term usage in this document controls.

[0074] IT rooms or data centers, the terms being used synonymously herein, may contain one or more types of IT equipment that may manipulate, receive, store, and / or transmit multiple forms of data including, for example, voice or video data. The IT equipment can be housed in IT racks. Instead or in addition, equipment such as power supplies, user interfaces, etc., may be mounted in IT racks in IT rooms. IT rooms may include cooling equipment for the IT equipment. The cooling equipment may be mounted in racks along with the IT equipment or provided as one or more separate units, sometimes referred to as coolers or computer room air conditioner (CRAC) or other forms of cooling units.

[0075] Systems and methods provided herein provide for determining a PUE ratio for a data center. Although the formula for PUE does not appear complex (PUE = Total Data Center Energy Usage / IT Equipment Energy Usage), in practice it may be difficult to obtain the inputs needed to calculate the actual PUE of a data center with high precision. For example, a data center may not have a dedicated power supply but may instead share power with other facilities, for example, offices at a particular site. There thus may not be any single power / energy meter that could be monitored to determine energy usage of the data center. Further, in some instances actual energy usage of the different equipment within a data center may not be known or measured on a regular basis. Accordingly, in many instances estimates of total data center energy usage and IT equipment energy usage are made based on information that is available, which may be incomplete, to give an estimate of data center PUE and a confidence interval or uncertainty in the estimated PUE.

[0076] Various computing devices may execute various operations discussed above. Using data stored in associated memory, in one example, a computer executes one or more instructions stored on one or more non-transitory computer-readable media that may result in manipulated data. In some examples, the computer may include one or more processors or other types of computing hardware. In one example, the computing hardware is or includes a commercially available, general-purpose processor. In another example, the computer performs at least a portion of the operations discussed herein using an application-specific integrated circuit (ASIC) tailored to perform particular operations in addition to, or in lieu of, a general-purpose processor. As illustrated by these examples, examples in accordance with the present invention may perform the operations described herein using many specific combinations of hardware and software and the invention is not limited to any particular combination of hardware and software components. In various examples, a computer may implement a multi-threading process to execute operations discussed herein.

[0077] Aspects disclosed herein in accordance with the present embodiments, are not limited in their application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. These aspects are capable of assuming other embodiments and of being practiced or of being carried out in various ways. Examples of specific implementations are provided herein for illustrative purposes only and are not intended to be limiting. In particular, acts, elements and features discussed in connection with any one or more embodiments are not intended to be excluded from a similar role in any other embodiments.

[0078] For example, according to one embodiment of the present invention, a computer system is configured to perform any of the functions described herein, including but not limited to, configuring, modeling, and presenting information regarding specific IT room configurations. The computer system may present the information to a user as a display of estimated PUE and uncertainty in the estimated PUE in a graphical user interface. Further, computer systems in embodiments may receive input from a user and / or directly from physical sensors in the data center that automatically measure environmental parameters and / or energy consumption of various equipment in an IT room. Moreover, the systems described herein may be configured to include or exclude any of the functions discussed herein. Thus, the embodiments are not limited to a specific function or set of functions. Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0079] Computer System

[0080] Various aspects and functions described herein in accordance with the present embodiments may be implemented as hardware or software on one or more computer systems. There are many examples of computer systems currently in use. These examples include, among others, network appliances, personal computers, workstations, mainframes, networked clients, servers, media servers, application servers, database servers, and web servers. Other examples of computer systems may include mobile computing devices, such as cellular phones and personal digital assistants, and network equipment, such as load balancers, routers and switches. Further, aspects in accordance with the present embodiments may be located on a single computer system or may be distributed among a plurality of computer systems connected to one or more communications networks. For example, various aspects and functions may be distributed among one or more computer systems configured to provide a service to one or more client computers, or to perform an overall task as part of a distributed system. Additionally, aspects may be performed on a client-server or multi-tier system that includes components distributed among one or more server systems that perform various functions. Thus, the embodiments are not limited to executing on any particular system or group of systems. Further, aspects may be implemented in software, hardware or firmware, or any combination thereof. Thus, aspects in accordance with the present embodiments may be implemented within methods, acts, systems, system elements and components using a variety of hardware and software configurations, and the embodiments are not limited to any particular distributed architecture, network, or communication protocol.

[0081] FIG. 1 shows a block diagram of a distributed computer system 100, in which various aspects and functions in accord with the present embodiments may be practiced. Distributed computer system 100 may include one more computer systems. For example, as illustrated, distributed computer system 100 includes computer systems 102, 104, and 106. As shown, computer systems 102, 104, and 106 are interconnected by, and may exchange data through, communication network 108. Network 108 may include any communication network through which computer systems may exchange data. To exchange data using network 108, computer systems 102, 104, and 106 and network 108 may use various methods, protocols and standards, including, among others, token ring, Ethernet, wireless Ethernet, Bluetooth, TCP / IP, UDP, Http, FTP, SNMP, SMS, MMS, SS7, Json, Soap, and Cobra. To ensure data transfer is secure, computer systems 102, 104, and 106 may transmit data via network 108 using a variety of security measures including TLS, SSL, or VPN among other security techniques. While distributed computer system 100 illustrates three networked computer systems, distributed computer system 100 may include any number of computer systems and computing devices, networked using any medium and communication protocol.

[0082] Various aspects and functions in accordance with the present embodiments may be implemented as specialized hardware or software executing in one or more computer systems including computer system 102 shown in FIG. 1. As depicted, computer system 102 includes processor 110, memory 112, bus 114, interface 116, and storage 118. Processor 110 may perform a series of instructions that result in manipulated data. Processor 110 may be a commercially available processor such as an Intel Core®, Motorola PowerPC, SGI MIPS, Sun UltraSPARC, or Hewlett-Packard PA-RISC processor, but may be any type of processor, multi-processor, microprocessor, or controller as many other processors and controllers are available. Processor 110 is connected to other system elements, including one or more memory devices 112, by bus 114.

[0083] Memory 112 may be used for storing programs and data during operation of computer system 102. Thus, memory 112 may be a relatively high performance, volatile, random access memory such as a dynamic random access memory (DRAM) or static memory (SRAM). However, memory 112 may include any device for storing data, such as a disk drive or other non-volatile, non-transitory, storage device. Various embodiments in accordance with the present invention may organize memory 112 into particularized and, in some cases, unique structures to perform the aspects and functions disclosed herein.

[0084] Components of computer system 102 may be coupled by an interconnection element such as bus 114. Bus 114 may include one or more physical busses, for example, busses between components that are integrated within a same machine, but may include any communication coupling between system elements including specialized or standard computing bus technologies such as IDE, SCSI, PCI, and InfiniBand. Thus, bus 114 enables communications, for example, data and instructions, to be exchanged between system components of computer system 102.

[0085] Computer system 102 also includes one or more interface devices 116 such as input devices, output devices, and combination input / output devices. Interface devices may receive input or provide output. More particularly, output devices may render information for external presentation. The interface devices 116 may include, for example, one or more graphical user interfaces that may be disposed proximate to or separate from other components of the computer system 102. A graphical user interface of the computer system 102 may, for example, be displayed through a web browser that accesses information from the memory 112. Input devices may accept information from external sources. Examples of interface devices include keyboards, mouse devices, trackballs, microphones, touch screens, printing devices, display screens, speakers, network interface cards, etc. Interface devices allow computer system 102 to exchange information and communicate with external entities, such as users and other systems. Other examples of input devices include temperature monitors internal to a data center or in the environment external to the data center, as well as power meters operatively couped to equipment within the data center, for example, power supplies (including uninterruptible power supplies (UPSs)), cooling providers, lighting systems, etc.

[0086] Storage system 118 may include a computer readable and writeable, nonvolatile, non- transitory, storage medium in which instructions are stored that define a program to be executed by the processor. The program to be executed by the processor may cause the processor 100 or computer system 102 to perform any one or more embodiments of the methods disclosed herein. Storage system 118 also may include information that is recorded, on or in, the medium, and this information may be processed by the program. More specifically, the information may be stored in one or more data structures specifically configured to conserve storage space or increase data exchange performance. The instructions may be persistently stored as encoded signals, and the instructions may cause a processor to perform any of the functions described herein. The medium may, for example, be optical disk, magnetic disk, or flash memory, among others. In operation, the processor or some other controller may cause data to be read from the nonvolatile recording medium into another memory, such as memory 112, that allows for faster access to the information by the processor than does the storage medium included in storage system 118. The memory may be located in storage system 118 or in memory 112, however, processor 110 may manipulate the data within the memory 112, and then may copy the data to the medium associated with storage system 118 after processing is completed. A variety of components may manage data movement between the medium and integrated circuit memory element and the presently described embodiments are not limited thereto. Further, the embodiments are not limited to a particular memory system or data storage system. Portions of the memory 112 or storage system 118 may be included in the same computer system as other components of the computer system 102 or may be resident in a cloud-based system that is accessible via the internet or other communications system or protocol.

[0087] Although computer system 102 is shown by way of example as one type of computer system upon which various aspects and functions in accordance with the present embodiments may be practiced, any aspects of the presently disclosed embodiments are not limited to being implemented on the computer system as shown in FIG. 1. Various aspects and functions in accordance with the presently disclosed embodiments may be practiced on one or more computers having a different architectures or components than that shown in FIG. 1. For instance, computer system 102 may include specially-programmed, specialpurpose hardware, for example, an application- specific integrated circuit (ASIC) tailored to perform a particular operation disclosed herein. Another embodiment may perform the same function using several general-purpose computing devices running MAC OS System X with Motorola PowerPC processors and several specialized computing devices running proprietary hardware and operating systems. Computer system 102 may be a computer system including an operating system that manages at least a portion of the hardware elements included in computer system 102. Usually, a processor or controller, such as processor 110, executes an operating system which may be, for example, a Windows-based operating system such as Windows 11 or Windows 10 operating systems, available from the Microsoft Corporation, a MAC OS System X operating system available from Apple Computer, one of many Linux-based operating system distributions, for example, the Enterprise Linux operating system available from Red Hat Inc., a Solaris operating system available from Sun Microsystems, or a UNIX operating system available from various sources. Many other operating systems may be used, and embodiments are not limited to any particular implementation.

[0088] The processor and operating system together define a computer platform for which application programs in high-level programming languages may be written. These component applications may be executable, intermediate, for example, C-, bytecode or interpreted code which communicates over a communication network, for example, the Internet, using a communication protocol, for example, TCP / IP. Similarly, aspects in accord with the presently disclosed embodiments may be implemented using an object-oriented programming language, such as .Net, SmallTalk, Java, C++, Ada, or C# (C-Sharp). Other object-oriented programming languages may also be used. Alternatively, functional, scripting, or logical programming languages may be used.

[0089] Additionally, various aspects and functions in accordance with the presently disclosed embodiments may be implemented in a non-programmed environment, for example, documents created in HTML, XML, or other format that, when viewed in a window of a browser program, render aspects of a graphical-user interface or perform other functions. Eurther, various embodiments in accord with the present invention may be implemented as programmed or non-programmed elements, or any combination thereof. Lor example, a web page may be implemented using HTML while a data object called from within the web page may be written in C++. Thus, the presently disclosed embodiments are not limited to a specific programming language and any suitable programming language could also be used.

[0090] A computer system included within an embodiment may perform additional functions outside the scope of the presently disclosed embodiments. Lor instance, aspects of the system may be implemented using an existing commercial product, such as, for example, Database Management Systems such as SQL Server available from Microsoft of Seattle WA., Oracle Database from Oracle of Redwood Shores, CA, and MySQL from MySQL AB, a subsidiary of Oracle or integration software such as Web Sphere middleware from IBM of Armonk, NY. However, a computer system running, for example, SQL Server may be able to support both aspects in accord with the presently disclosed embodiments and databases for sundry applications.

[0091] Example System Architecture

[0092] FIG. 2 presents a context diagram including physical and logical elements of distributed system 200. As shown, distributed system 200 is specially configured in accordance with the presently disclosed embodiments. The system structure and content recited with regard to FIG. 2 is for exemplary purposes only and is not intended to limit the embodiments to the specific structure shown in FIG. 2. As will be apparent to one of ordinary skill in the art, many variant system structures can be architected without deviating from the scope of the presently disclosed embodiments. The particular arrangement presented in FIG. 2 was chosen to promote clarity.

[0093] Information may flow between the elements, components, and subsystems depicted in FIG. 2 using any technique. Such techniques include, for example, passing the information over the network via TCP / IP, passing the information between modules in memory and passing the information by writing to a file, database, or some other non-volatile storage device. Other techniques and protocols may be used without departing from the scope of the presently disclosed embodiments.

[0094] Referring to FIG. 2, system 200 includes user 202, interface 204, IT room design and management system 206, communications network 208, and IT room database 210. System 200 may allow user 202, such as an IT room architect or other IT room personnel, to interact with interface 204 to create or modify a model of one or more IT room configurations. System 200 may allow user 202 to interact with interface 204 to view a graphical display of results of embodiments of models disclosed herein. According to one embodiment, interface 204 may include aspects of the floor editor and the rack editor as disclosed in Patent Cooperation Treaty Application No. PCT / US08 / 63675, titled “Methods and Systems for Managing Facility Power and Cooling,” filed on May 15, 2008, which is incorporated herein by reference in its entirety and is hereinafter referred to as PCT / US08 / 63675. In other embodiments, interface 204 may be implemented with specialized facilities that enable user 202 to design, in a drag and drop fashion, a model that includes a representation of the physical layout of an IT room or any subset thereof. This layout may include representations of IT room structural components as well as IT room equipment. The features of interface 204, as may be found in various embodiments in accordance with the present invention, are discussed further below. In at least one embodiment, information regarding an IT room is entered into system 200 through the interface via manual data entry and / or by accessing data from one or more sensors present in an IT room, and assessments and recommendations for the IT room are provided to the user. Further, in at least one embodiment, optimization processes may be performed to optimize energy usage of the IT room. Optimization processes may be based on simulated values of one or more parameters in an IT room, values of one or more measured parameters in an IT room, or a combination of both.

[0095] As shown in FIG. 2, IT room design and management system 206 presents data design interface 204 to user 202. According to one embodiment, IT room design and management system 206 may include aspects of the IT room design and management system as disclosed in PCT / US08 / 63675. In this embodiment, design interface 204 may incorporate functionality of the input module, the display module and the builder module included in PCT / US08 / 63675 and may use the database module to store and retrieve data.

[0096] As illustrated, IT room design and management system 206 may exchange information with IT room database 210 via network 208. This information may include any information needed to support the features and functions of IT room design and management system 206. For example, in one embodiment, IT room database 210 may include at least some portion of the data stored in an IT room equipment database such as described in PCT / US08 / 63675. In another embodiment, this information may include any information needed to support interface 204, such as, among other data, the physical layout of one or more IT room model configurations, the production and distribution characteristics of the cooling providers included in the model configurations, the consumption characteristics of the cooling consumers in the model configurations, and a listing of equipment racks and cooling providers to be included in a cluster.

[0097] In one embodiment, IT room database 210 may store types of cooling providers, the amount of cool air provided by each type of cooling provider, and a temperature of cool air provided by the cooling provider. Thus, for example, IT room database 210 includes records of a particular type of computer room air conditioning (CRAC) unit that is rated to deliver airflow at the rate of 5,600 cubic feet per minute (cfm) at a temperature of 68 degrees Fahrenheit. In addition, the IT room database 210 may store one or more measured or simulated cooling metrics, such as inlet and outlet temperatures of the CRACs and inlet and exhaust temperatures of one or more equipment racks. The temperatures may be periodically measured and input into the system, or in other embodiments, the temperatures may be continuously monitored using devices coupled to the system 200. IT room database 210 may take the form of any logical construction capable of storing information on a computer readable medium including, among other structures, flat files, indexed files, hierarchical databases, relational databases or object oriented databases. The data may be modeled using unique and foreign key relationships and indexes. The unique and foreign key relationships and indexes may be established between the various fields and tables to ensure both data integrity and data interchange performance.

[0098] The computer systems shown in FIG. 2, which include IT room design and management system 206, network 208, and IT room equipment database 210, each may include one or more computer systems. As discussed above with regard to FIG. 1, computer systems may have one or more processors or controllers, memory, and interface devices. The particular configuration of system 200 depicted in FIG. 2 is used for illustration purposes only and embodiments of the invention may be practiced in other contexts. Thus, embodiments of the invention are not limited to a specific number of users or systems.

[0099] Aspects and embodiments disclosed herein include methods of performing calculation of estimated PUE within a proposed or existing IT room. These methods may be performed on embodiments of the computer systems disclosed herein and may be represented by code recorded on non-volatile non-transitory computer readable medium that may be read by embodiments of the computer systems disclosed herein.

[0100] Systems and methods disclosed herein may be configured to perform adaptive PUE calculations for a data center. The adaptive PUE calculation allows for the calculation of an estimated PUE from a minimal set of input parameter about a datacenter without any actual power measurements - assuming that the datacenter is build using best practices. Additional input parameters may be added to improve the accuracy and reduce the uncertainty in the estimated PUE value.

[0101] The calculator allows for the inclusion of live measurements or parameters such as power input or output to and from different equipment in a data center and will automatically include these in the PUE calculation, thereby making the PUE calculation more precise. The calculated PUE is provided with a confidence interval.

[0102] The adaptive PUE calculator models a data center (an energy system) as a set of loosely coupled functional components. This allows the model to adapt to any information that can be provided about the data center. Using functional components provides for estimating power loses in the data center without knowing the actual power path / line diagram (for example, the presence of absence of a generator, a cooling tower, variable speed pumps, etc.) In the context of IT equipment, where power consumption of data center devices is measured, the adaptive PUE calculator will use actual live measurements to extract the information and data it needs to produce the PUE. If actual total data center energy usage and total actual IT equipment energy usage are part of the data available to the model, it will produce an exact PUE. In other cases the calculated PUE will represent a mix between a theoretical PUE and actual measured data.

[0103] The systems and methods disclosed herein can provide a data center customer with a reasonable estimate of the PUE of their data center. As the customer enters more detailed information or the system and method discovers details in customers data, the calculated PUE will automatically be corrected (made more precise).

[0104] Aspects and embodiments disclosed herein may include building a very general model that will calculate the PUE of any data center build using current best practices. As more information and data become available the model will adapt to this information and provide better estimates of PUE. Data and information can be made available through the addition of more hardware monitoring, updates to firmware, etc.

[0105] By taking unknown parameters in the PUE calculation to (reasonable) extremes, the systems and methods disclosed herein can calculate a certainty interval for the calculated PUE indicating a confidence level in the calculated value.

[0106] Aspects and embodiments disclosed herein include providing data center customers with a sustainability dashboard containing, among other information, the PUE and powerbreakdown per customer site. An example of such a dashboard is illustrated in FIG. 3. Another example of a dashboard that may be made available to a data center customer may include trends in PUE over time and other statistics regarding PUE at a number of a customer’s sites as shown in FIG. 4.

[0107] To build a first estimate of PUE for a customer’s data center, the systems and methods disclosed herein may include prompting the customer to enter information regarding the configuration of their data center, including types and quantities of different forms of equipment in the data center, geographical location of the data center, etc. A non-exhaustive list of the information that the customer may be prompted to provide may include:

[0108] - power redundancy (N, N+l, or 2N power path)

[0109] - cooling redundancy (N, N+l, or 2N cooling)

[0110] - cooling medium (No cooling, Chilled water, Air cooled, Direct expansion, Liquid cooled)

[0111] - data center location (longitude / latitude) - design capacity of the data center energy system

[0112] - the design rack density (l-20kW per rack)

[0113] - are the PDU’s equipped with step-down transformers (400V->208V)

[0114] - do pumps in the cooling loop have variable frequency drive

[0115] - how many hours a year is the cooling system ‘free cooling’

[0116] - what type of UPSs are used (Typical / High efficiency)

[0117] - is the lighting high efficiency

[0118] - what is the average outdoor temperature

[0119] - what is the temperature in the datacenter / energy system

[0120] Based on the type of cooling medium utilized, a non-exhaustive list of additional information the customer may be prompted to provide may include:

[0121] *Chilled water*

[0122] - cooling tower (yes / no)

[0123] - packaged chiller (yes / no)

[0124] - fans on critical power path (yes / no)

[0125] *Air cooled*

[0126] - CRAC with external fans (yes / no)

[0127] - CRAC on critical power path (yes / no)

[0128] *Direct expansion*

[0129] - CRAC with external fans (yes / no)

[0130] - CRAC on critical power path (yes / no)

[0131] If a customer chooses not to provide complete information regarding their data center configuration, the systems and methods disclosed herein may include a “best guess” for the data center configuration based on industry best practices. A representation of a data center generated based on user input or equipment assumed by the model may be similar to that shown in FIG. 5 A or in FIG. 5B.

[0132] In the data center configuration of FIG. 5A power is supplied to the data center from either utility mains 205 or a generator 210. The generator 210 may be utilized if there is some problem with the utility mains 205. The power is received by switchgear 215 that routes the power into a power bus 220. The power bus 220 provides power to one or more UPSs 225 that pass the power on to one or more PDUs 230. The PDUs 230 distribute power through wiring 235 to IT equipment 240 within the data center. Power is also supplied to a cooling system 245 for the data center, either directly from the power bus 220 or through one or more UPSs 225. FIG. 5B is an alternative view illustrating the data center as an energy system containing the power-consuming equipment used to support the IT equipment of the system. Power E enters the system into a high level power distribution node 305 which may include, for example, the switchgear 215, power bus 220, UPSs 225, and PDUs 230 illustrated in FIG. 5A. Power from the high level power distribution node 305 flows through a critical power distribution path 310 to the IT equipment 240. Power from the high level power distribution node 305 also flows into supporting equipment 315, for example, lighting, as well as the cooling system 245. Unused power Q may exit the energy system.

[0133] The data center may also be modelled as a system of energy busses as illustrated in FIG. 5C. The busses may include a utility bus 405, a primary bus 410, a critical bus 415, and an indoor heat bus 420. Sitchgear 215 may be used to distribute power between the utility bus 405 and primary bus 410, with the UPSs 225 providing some power from the primary bus 410 to the critical bus 415. In the multiple bus-based model, the individual subsystems (e.g., generator 210, auxiliary devices 425, lighting 430, cooling 245, and IT load 240) can be connected to different busses, based on the actual configuration of the datacenter. The indoor heat bus 240 (heat for short) is special in that it implicitly ‘connects’ to all subsystems that dissipate heat inside the datacenter. The function of the heat bus is to capture the cooling requirements in a simple concise way that makes it possible to include in the calculation. Alternative bus-based model configurations are illustrated in FIGS. 5D and 5E.

[0134] FIG. 5D illustrates a configuration in which the generator 210, some auxiliary devices 425, lighting 430, cooling (CRAC 245A and ventilation 245B) are connected to the primary bus 410. Other auxiliary devices 425, PDUs 230, and the IT load 240 are connected to the critical bus 415.

[0135] The configuration of FIG. 5E is similar to that of FIG. 5D, but the system is modelled without a generator and the lighting 430 has been moved to the critical bus 415.

[0136] Calculating the PUE is then a question of calculating the energy consumption on the utility bus and on the IT Load subsystem:

[0137] PUE = Eutiiity / Eir_ Load

[0138] A first estimate of data center PUE may be calculated based on the information provided by the customer or assumed by the system and method based on industry best practices by obtaining power consumption information from preexisting models of the equipment and data center configurations including the specified or assumed equipment. Models of ranges of energy consumption and / or efficiency for the different items of equipment in the modelled data center may be utilized to determine an uncertainty or range of possible values of the PUE for the data center.

[0139] In many instances, the rating of all the UPSs in a given data center may be know from the UPS model number. From this, the power rating (design capacity) may be calculated. If a power rating of a given UPS is unknown it may be estimated.

[0140] Once a realistic estimate of the IT design capacity is known the IT load and the PU load can be found using the strategy illustrated in FIG. 6. These calculations are done per UPS and if the data center contains more than one UPS, the rates and IT_loads of the individual UPSs are summed, and the PU_load (PU Eoad = IT Eoadactuai / IT Eoadpianned capacity) and a convenient way of abstracting the actual size of the datacenter out of the equations behind the component losses may be found on the basis of these accumulations:

[0141] The approach outlined above has at least one drawback - it requires a complete configuration of the datacenter. Since this may not is all instances be possible, aspects and embodiments disclosed herein may utilize techniques to circumvent this requirement.

[0142] One possible way is to bound the configuration and hence the PUE calculation from above and below such that the actual PUE will be between the two bounds.

[0143] To do this a worst-case and a best-case configuration may be defined and utilized as the basis for the upper and lower bounds of the PUE calculations.

[0144] As more knowledge is gained about the datacenter being analyzed, the uncertainties of upper and lower bound configurations are reduced, and the calculated upper and lower bounds on the PUE estimate should converge towards the actual PUE as shown in the graph of FIG. 7.

[0145] One advantage of this approach is that a rough estimate of PUE together with likely upper and lower bounds may be provided even with very little data. Additionally, the model automatically adapts to the provision of more information regarding configuration of a data center by providing more precise estimates with less uncertainties (upper and lower bounds move closer). A different way of visualizing this can be seen in FIG. 8, where the worst / best case curves for the PUE calculation are plotted against the PU load. From FIG. 8, it may be seen that the greatest uncertainty is found at low PU load with little or no knowledge of the data center giving a PUE range of, in a non-limiting example, 2.05 to 23.75, whereas running the data center at full capacity with knowledge about UPSs and cooling type reduces the uncertainty range to a non-limiting example of 1.60-2.04.

[0146] In some embodiments the model assumes that a data center being analyzed been designed “appropriately” such that the sizing of power infrastructure and cooling correspond with the design load of the data center. Modelling of power consumption of the cooling system of the data center may then be approximated by modelling of the UPS load.

[0147] In examples of the systems and methods disclosed herein, a “component” may be considered a representation of a physical device (or the ‘average’ over a collection of devices). In the PUE calculation, components represent a point where energy is potentially transformed or mediated and as a consequence of this there will be an energy loss (as thermal energy).

[0148] In the PUE calculator, losses in components are modelled as: loss = Ax2+ Bx + C

[0149] Where A, B and C are ‘loss characteristics’ specific for that component and x is the load factor of the component expressed as a PU fraction or PU Load.

[0150] Different subsystems will use different components since their loss is directly related to the physical equipment in the data center. Each of the different ‘types’ of subsystem have a dedicated builder to help construct the subsystem and embed the required logic to evaluate the energy flow of the subsystem. The builders are responsible for finding the correct loss parameters for the subsystem by selecting the appropriate component.

[0151] Besides the calculated PUE, pPUEpOwer, and pPUECOoiing, the calculations performed by systems and methods disclosed herein will also output the PUEcontext containing the subsystems and their individual contribution to the energy flow.

[0152] In an operating data center, measurements of actual operating parameters may be obtained from appropriate sensors and provided to the model for refining the PUE calculation and reducing the uncertainty in the calculated PUE. A non-exhaustive list of such measured parameters and monitoring equipment may include:

[0153] - Temperature probes inside data center

[0154] - Temperature probes outside of data center

[0155] - UPS measurements - rated power

[0156] - load

[0157] - input power

[0158] - output power

[0159] - Power monitor

[0160] - energy consumption

[0161] - Cooling equipment

[0162] - energy consumption

[0163] Values of parameters that are not measured may be inferred from other measured parameters. For example, any one of the UPS measurements listed above may be inferred if others of these measurements are available. IT equipment power may be inferred from UPS output power. Cooling equipment energy consumption may be inferred from IT equipment load, optionally along with temperatures inside and outside the data center.

[0164] When a value cannot be inferred, it may be “replaced” by a best case / worst case value pair which may be included in the calculation of the upper and lower bounds (confidence interval) of the calculated data center PUE value.

[0165] An example of a method that may be performed by systems disclosed herein is shown in the flowchart of FIG. 9. The method begins in act 905 in which a user is prompted to identify equipment within a proposed or existing data center. The user provides the indication of equipment though a user interface of the system in act 910. Types of equipment the user may be prompted to identify may include any one or more of, for example, power supplies (e.g., UPSs), cooling systems, lighting systems, IT systems, etc. The user may also be prompted for additional information about the data center in acts 905 and 910, for example, location, average temperature outside the data center, average temperature inside the data center, etc. The system may build a model of the data center based on the information provided by the user.

[0166] In act 915 the system queries the user if they have identified all quantities and types of equipment in the data center that they wish to. If the user answers “yes” but there are certain quantities or types of equipment that the user did not identify that would be expected to be in a typical data center, the system may select from a range of possible forms or models of the type(s) of equipment the user did not identify to include in the data center model. These additional type(s) of equipment may be considered “inferred” equipment types.

[0167] In act 920, the system calculates an estimate of the PUE of the data center. The system may obtain operational parameters, for example, power consumption parameters of the equipment included in the model of the data center from one or more predetermined data center models or specifications for the equipment in the data center model. Values of these operational parameters exhibited by the equipment in actual operation may have some ranges, which may contribute to some uncertainty in the estimated PUE. Further, if the data center model includes any inferred equipment, the system may model the values of the operational parameters of the inferred equipment as the broadest range that might be expected, or as a range that encompasses values of operational parameters of any or all of the equipment that might be utilized for the equipment the user did not specify. These ranges in values of operational parameters, e.g., ranges of possible power consumption of the different equipment may be used to calculate an uncertainty in the estimated PUE. In some embodiments, the estimated PUE and its uncertainty may be displayed to a user. If the user wishes to obtain a more accurate PUE with less uncertainty, they may specify additional equipment quantities and / or type(s) to use in the data center model instead of the inferred equipment or they may input tighter ranges of expected values of operational parameters for one or more of the types of equipment and the system may update the data center model and estimated PUE and its uncertainty.

[0168] Once the user has input indications of quantities and types of all equipment in the data center that they wish, and if the data center is operational, the system may obtain actual operational parameters from the equipment in the data center (act 925). These measured operational parameters may include any one or more of, for example, load on a power supply from an electrical load sensor operatively connected to the power supply, input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply, temperature inside the data center, temperature of an environment external to the data center, etc. In some embodiments, some parameters, for example, load on a cooling system may be inferred from other parameters, for example, output power from power supplies in the data center. Once all parameters to which sensors of the system have access to have been measured (acts 925 and 930), the data center model may be refined to more specifically define the values of the operational parameters of the equipment in the data center. The refined data center model may then be utilized to calculate a refined PUE estimate and a refined uncertainty in the PUE estimate (act 935) that may be presented to the user in a user interface of the system.

[0169] In at least some embodiments described above, tools and processes are provided for determining PUE in an IT room. In other embodiments, the tools and processes may be used in other types of facilities, and may also be used in mobile applications, including mobile IT rooms.

[0170] Having thus described several aspects of at least one embodiment of this invention, it is to be appreciated various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure and are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description and drawings are by way of example only.

[0171] Further examples are provided in the following clauses:

[0172] Clause 1. A method of performing an adaptive calculation of power usage efficiency (PUE) of a data center, the method comprising receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of values associated with the parameters of the one or more items of equipment in the data center, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

[0173] Clause 2. The method of clause 1, further comprising presenting the estimated PUE and the first measure of uncertainty to the user.

[0174] Clause 3. The method of clause 1, further comprising receiving readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

[0175] Clause 4. The method of clause 3, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

[0176] Clause 5. The method of clause 3, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

[0177] Clause 6. The method of clause 4 or clause 5, further comprising inferring a cooling system power load from one or more loads on the power supply, the input power to the power supply, or the output power from the power supply.

[0178] Clause 7. The method of clause 3, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of temperature inside the data center or temperature of an environment external to the data center.

[0179] Clause 8. The method of clause 1, wherein determining the estimated PUE and a measure of uncertainty in the estimated PUE includes obtaining power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

[0180] Clause 9. The method of clause 1, wherein receiving the input defining the quantities and types of equipment in the data center includes prompting a user to identify the quantities and types of equipment in the data center.

[0181] Clause 10. The method of clause 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of power supplies in the data center.

[0182] Clause 11. The method of clause 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of cooling systems in the data center.

[0183] Clause 12. The method of clause 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of lighting systems in the data center.

[0184] Clause 13. The method of clause 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of information technology (IT) systems in the data center.

[0185] Clause 14. The method of clause 1, wherein refining the estimated PUE and measure of uncertainty in the estimated PUE includes determining the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

[0186] Clause 15. The method of clause 1, further comprising calculating a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

[0187] - 1 - Clause 16. The method of clause 1, further comprising receiving an indication of a location of the data center and utilizing the location as a factor in determining the estimated PUE.

[0188] Clause 17. A method of performing an adaptive calculation of power usage efficiency (PUE) of a data center, the method comprising receiving a first input defining quantities and types of equipment in the data center, creating a model of the data center from the first input, determining if the first input omits one or more types of equipment expected to be present in the data center, responsive to determining that the first input omits one or more of the types of equipment expected to be present in the data center, determine a most likely type or types of the one or more types of equipment expected to be present in the data center and omitted from the first input as one or more inferred equipment types, values of operational parameters of the one or more inferred equipment types being modelled as spanning a range encompassing values of operational parameters of all of the most likely type or types of the one or more types of equipment, adding any inferred equipment types to the model of the data center, determining an estimated PUE and a measure of uncertainty in the estimated PUE from the model of the data center, receiving a second input defining quantities and types of equipment in the data center omitted from the first input, responsive to one or more of the types of equipment received in the second input being a functional equivalent to one or more of the inferred equipment types, updating the model of the data center by substituting any inferred equipment types with the functional equivalent equipment types, determining an updated estimated PUE and an updated measure of uncertainty in the updated estimated PUE responsive to updating the model of the data center, and presenting the updated estimated PUE and measure of uncertainty in the updated estimated PUE to a user.

[0189] Clause 18. The method of clause 17, further comprising receiving readings of operational parameters of one or more items of the equipment in the data center from one or more sensors operatively connected to the one or more items of the equipment, refining one of the estimated PUE and measure of uncertainty in the estimated PUE or the updated estimated PUE and measure of uncertainty in the updated estimated PUE based on the received readings to obtain a refined PUE and measure of uncertainty in the refined PUE, and presenting the refined PUE and measure of uncertainty in the refined PUE to a user.

[0190] Clause 19. A system configured to perform an adaptive calculation of power usage efficiency (PUE) of a data center, the system comprising a controller configured to receive a first input defining quantities and types of equipment in the data center, determine an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receive a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refine the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of values associated with the parameters of the one or more items of equipment in the data center, and present the refined PUE and the second measure of uncertainty in the refined PUE to a user.

[0191] Clause 20. The system of clause 19, wherein the controller further is configured to present the estimated PUE and the first measure of uncertainty to the user.

[0192] Clause 21. The system of clause 19, wherein the controller further is configured to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

[0193] Clause 22. The system of clause 21, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

[0194] Clause 23. The system of clause 21, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

[0195] Clause 24. The system of clause 22 or clause 23, wherein the controller is further configured to infer a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

[0196] Clause 25. The system of clause 21, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of temperature inside the data center or temperature of an environment external to the data center.

[0197] Clause 26. The system of clause 19, wherein determining the estimated PUE and a measure of uncertainty in the estimated PUE includes obtaining power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models. Clause 27. The system of clause 19, wherein receiving the input defining the quantities and types of equipment in the data center includes prompting a user to identify the quantities and types of equipment in the data center.

[0198] Clause 28. The system of clause 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of power supplies in the data center.

[0199] Clause 29. The system of clause 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of cooling systems in the data center.

[0200] Clause 30. The system of clause 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of lighting systems in the data center.

[0201] Clause 31. The system of clause 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of information technology (IT) systems in the data center.

[0202] Clause 32. The system of clause 19, wherein refining the estimated PUE and measure of uncertainty in the estimated PUE includes determining the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

[0203] Clause 33. The system of clause 19, wherein the controller further is configured to calculate a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

[0204] Clause 34. The system of clause 19, wherein the controller further is configured to receive an indication of a location of the data center and utilizing the location as a factor in determining the estimated PUE.

[0205] Clause 35. A non-transitory computer readable medium having instructions encoded therein which when executed by a computer system cause the computer system to perform a method comprising receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of the values associated with the parameters of the one or more items of equipment in the data center, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

[0206] Clause 36. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to present the estimated PUE and the first measure of uncertainty to the user.

[0207] Clause 37. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

[0208] Clause 38. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to determine the estimated PUE and the measure of uncertainty in the estimated PUE from power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

[0209] Clause 39. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to prompt a user to identify the quantities and types of equipment in the data center.

[0210] Clause 40. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of power supplies in the data center.

[0211] Clause 41. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of cooling systems in the data center.

[0212] Clause 42. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of lighting systems in the data center.

[0213] Clause 43. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of information technology (IT) systems in the data center.

[0214] Clause 44. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to receive an indication of load on a power supply from an electrical load sensor operatively connected to the power supply. Clause 45. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to receive an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

[0215] Clause 46. The non-transitory computer readable medium of clause 44 or 45, wherein the instructions further cause the computer system to infer a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

[0216] Clause 47. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to receive an indication of one of temperature inside the data center or temperature of an environment external to the data center.

[0217] Clause 48. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to determine the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

[0218] Clause 49. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to determine a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

[0219] Clause 50. The non-transitory computer readable medium of clause 35, wherein the instructions further cause the computer system to receive an indication of a location of the data center and utilize the location as a factor in determining the estimated PUE.

[0220] Clause 51. A non-transitory computer readable medium having instructions encoded therein which when executed by a computer system cause the computer system to perform a method comprising receiving a first input defining quantities and types of equipment in the data center, creating a model of the data center from the first input, determining if the first input omits one or more types of equipment expected to be present in the data center, responsive to determining that the first input omits one or more of the types of equipment expected to be present in the data center, determine a most likely type or types of the one or more types of equipment expected to be present in the data center and omitted from the first input as one or more inferred equipment types, values of operational parameters of the one or more inferred equipment types being modelled as spanning a range encompassing values of operational parameters of all of the most likely type or types of the one or more types of equipment, adding any inferred equipment types to the model of the data center, determining an estimated PUE and a measure of uncertainty in the estimated PUE from the model of the data center, receiving a second input defining quantities and types of equipment in the data center omitted from the first input, responsive to one or more of the types of equipment received in the second input being a functional equivalent to one or more of the inferred equipment types, updating the model of the data center by substituting any inferred equipment types with the functional equivalent equipment types, determining an updated estimated PUE and an updated measure of uncertainty in the updated estimated PUE responsive to updating the model of the data center, and presenting the updated estimated PUE and measure of uncertainty in the updated estimated PUE to a user.

[0221] Clause 52. The non-transitory computer readable medium of clause 51, wherein the instructions further cause the computer system to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors operatively connected to the one or more items of the equipment, refine one of the estimated PUE and measure of uncertainty in the estimated PUE or the updated estimated PUE and measure of uncertainty in the updated estimated PUE based on the received readings to obtain a refined PUE and measure of uncertainty in the refined PUE, and present the refined PUE and measure of uncertainty in the refined PUE to a user.

Claims

What is claimed is:CLAIMS1. A method of performing an adaptive calculation of power usage efficiency (PUE) of a data center, the method comprising: receiving a first input defining quantities and types of equipment in the data center; determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center; receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center; refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of values associated with the parameters of the one or more items of equipment in the data center; and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

2. The method of claim 1, further comprising presenting the estimated PUE and the first measure of uncertainty to the user.

3. The method of claim 1, further comprising receiving readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

4. The method of claim 3, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

5. The method of claim 3, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indicationof one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

6. The method of claim 4 or claim 5, further comprising inferring a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

7. The method of claim 3, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of temperature inside the data center or temperature of an environment external to the data center.

8. The method of claim 1, wherein determining the estimated PUE and a measure of uncertainty in the estimated PUE includes obtaining power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

9. The method of claim 1, wherein receiving the input defining the quantities and types of equipment in the data center includes prompting a user to identify the quantities and types of equipment in the data center.

10. The method of claim 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of power supplies in the data center.

11. The method of claim 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of cooling systems in the data center.

12. The method of claim 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of lighting systems in the data center.

13. The method of claim 1, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of information technology (IT) systems in the data center.

14. The method of claim 1, wherein refining the estimated PUE and measure of uncertainty in the estimated PUE includes determining the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

15. The method of claim 1, further comprising calculating a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

16. The method of claim 1, further comprising receiving an indication of a location of the data center and utilizing the location as a factor in determining the estimated PUE.

17. A method of performing an adaptive calculation of power usage efficiency (PUE) of a data center, the method comprising: receiving a first input defining quantities and types of equipment in the data center; creating a model of the data center from the first input; determining if the first input omits one or more types of equipment expected to be present in the data center; responsive to determining that the first input omits one or more of the types of equipment expected to be present in the data center, determine a most likely type or types of the one or more types of equipment expected to be present in the data center and omitted from the first input as one or more inferred equipment types, values of operational parameters of the one or more inferred equipment types being modelled as spanning a range encompassing values of operational parameters of all of the most likely type or types of the one or more types of equipment; adding any inferred equipment types to the model of the data center; determining an estimated PUE and a measure of uncertainty in the estimated PUE from the model of the data center; receiving a second input defining quantities and types of equipment in the data center omitted from the first input;responsive to one or more of the types of equipment received in the second input being a functional equivalent to one or more of the inferred equipment types, updating the model of the data center by substituting any inferred equipment types with the functional equivalent equipment types; determining an updated estimated PUE and an updated measure of uncertainty in the updated estimated PUE responsive to updating the model of the data center; and presenting the updated estimated PUE and measure of uncertainty in the updated estimated PUE to a user.

18. The method of claim 17, further comprising: receiving readings of operational parameters of one or more items of the equipment in the data center from one or more sensors operatively connected to the one or more items of the equipment; refining one of the estimated PUE and measure of uncertainty in the estimated PUE or the updated estimated PUE and measure of uncertainty in the updated estimated PUE based on the received readings to obtain a refined PUE and measure of uncertainty in the refined PUE; and presenting the refined PUE and measure of uncertainty in the refined PUE to a user.

19. A system configured to perform an adaptive calculation of power usage efficiency (PUE) of a data center, the system comprising: a controller configured to receive a first input defining quantities and types of equipment in the data center, determine an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receive a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refine the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of values associated with the parameters of the one or more items of equipment in the data center, andpresent the refined PUE and the second measure of uncertainty in the refined PUE to a user.

20. The system of claim 19, wherein the controller further is configured to present the estimated PUE and the first measure of uncertainty to the user.

21. The system of claim 19, wherein the controller further is configured to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

22. The system of claim 21, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

23. The system of claim 21, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

24. The system of claim 22 or claim 23, wherein the controller is further configured to infer a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

25. The system of claim 21, wherein receiving the readings of the operational parameters of the one or more items of the equipment in the data center includes receiving an indication of one of temperature inside the data center or temperature of an environment external to the data center.

26. The system of claim 19, wherein determining the estimated PUE and a measure of uncertainty in the estimated PUE includes obtaining power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

27. The system of claim 19, wherein receiving the input defining the quantities and types of equipment in the data center includes prompting a user to identify the quantities and types of equipment in the data center.

28. The system of claim 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of power supplies in the data center.

29. The system of claim 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of cooling systems in the data center.

30. The system of claim 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of lighting systems in the data center.

31. The system of claim 19, wherein receiving the input defining the quantities and types of equipment in the data center includes receiving from a user an indication of a quantity and type of information technology (IT) systems in the data center.

32. The system of claim 19, wherein refining the estimated PUE and measure of uncertainty in the estimated PUE includes determining the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

33. The system of claim 19, wherein the controller further is configured to calculate a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

34. The system of claim 19, wherein the controller further is configured to receive an indication of a location of the data center and utilizing the location as a factor in determining the estimated PUE.

35. A non-transitory computer readable medium having instructions encoded therein which when executed by a computer system cause the computer system to perform a method comprising: receiving a first input defining quantities and types of equipment in the data center; determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the input using a range of possible values associated with parameters of one or more items of equipment in the data center; receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center; refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input instead of a complete range of the values associated with the parameters of the one or more items of equipment in the data center; and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

36. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to present the estimated PUE and the first measure of uncertainty to the user.

37. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors, wherein refining the estimated PUE and the measure of uncertainty in the estimated PUE further comprises refining the estimated PUE and the measure of uncertainty in the estimated PUE based on the received readings.

38. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to determine the estimated PUE and the measure of uncertainty in the estimated PUE from power consumption estimates for the quantities and types of equipment in the data center from one or more predefined models.

39. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to prompt a user to identify the quantities and types of equipment in the data center.

40. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of power supplies in the data center.

41. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of cooling systems in the data center.

42. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of lighting systems in the data center.

43. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to prompt a user to identify a quantity and type of information technology (IT) systems in the data center.

44. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to receive an indication of load on a power supply from an electrical load sensor operatively connected to the power supply.

45. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to receive an indication of one of input power to a power supply or output power from the power supply from an electrical power sensor operatively connected to the power supply.

46. The non-transitory computer readable medium of claim 44 or 45, wherein the instructions further cause the computer system to infer a cooling system power load from one or more load on the power supply, the input power to the power supply, or the output power from the power supply.

47. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to receive an indication of one of temperature inside the data center or temperature of an environment external to the data center.

48. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to determine the measure of uncertainty in the refined PUE with a lower uncertainty than the measure of uncertainty in the estimated PUE.

49. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to determine a measure of uncertainty in the estimated PUE with a lower amount of uncertainty responsive to receiving additional input defining the quantities and types of equipment in the data center.

50. The non-transitory computer readable medium of claim 35, wherein the instructions further cause the computer system to receive an indication of a location of the data center and utilize the location as a factor in determining the estimated PUE.

51. A non-transitory computer readable medium having instructions encoded therein which when executed by a computer system cause the computer system to perform a method comprising: receiving a first input defining quantities and types of equipment in the data center; creating a model of the data center from the first input; determining if the first input omits one or more types of equipment expected to be present in the data center; responsive to determining that the first input omits one or more of the types of equipment expected to be present in the data center, determine a most likely type or types of the one or more types of equipment expected to be present in the data center and omitted from the first input as one or more inferred equipment types, values of operational parameters of the one or more inferred equipment types being modelled as spanning a range encompassing values of operational parameters of all of the most likely type or types of the one or more types of equipment; adding any inferred equipment types to the model of the data center; determining an estimated PUE and a measure of uncertainty in the estimated PUE from the model of the data center; receiving a second input defining quantities and types of equipment in the data center omitted from the first input;responsive to one or more of the types of equipment received in the second input being a functional equivalent to one or more of the inferred equipment types, updating the model of the data center by substituting any inferred equipment types with the functional equivalent equipment types; determining an updated estimated PUE and an updated measure of uncertainty in the updated estimated PUE responsive to updating the model of the data center; and presenting the updated estimated PUE and measure of uncertainty in the updated estimated PUE to a user.

52. The non-transitory computer readable medium of claim 51, wherein the instructions further cause the computer system to: receive readings of operational parameters of one or more items of the equipment in the data center from one or more sensors operatively connected to the one or more items of the equipment; refine one of the estimated PUE and measure of uncertainty in the estimated PUE or the updated estimated PUE and measure of uncertainty in the updated estimated PUE based on the received readings to obtain a refined PUE and measure of uncertainty in the refined PUE; and present the refined PUE and measure of uncertainty in the refined PUE to a user.

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