The hospitality industry is increasingly focusing on better energy management as it looks to improve guest comfort levels while optimizing energy consumption. This paper talks about the data analytics-based approach to determine Operational Effectiveness and Energy Management (OE&EM) for the hospitality industry.
A Big Data Approach to Energy Management in RetailJack Watson
Retail businesses spend billions of dollars each year on energy. The industry faces the dual challenge of not only reducing carbon footprint, but also imbibing sustainable strategies that balance business objectives with environmental responsibilities. This paper discusses how the effective usage of Big Data Analytics can revolutionize energy sustainability initiatives for retailers, driving consumption pattern analysis, establishing efficiency blueprints and supporting maintenance efforts.
The CorPeuM mission is to improve the execution of strategy!
The basis of all performance management is in administering an organization’s business activities (sales, marketing, production, product development, etc.) in an environment that is increasingly uncertain. As outlined in ‘What is Strategy Execution?’, a strategy execution system should support the way in which these business processes are planned and monitored. This will require a number of integrated application capabilities, including:
Business Modelling: The system should be able to model an organization’s current and proposed business processes that show how they are connected to achieve the organization’s purpose.
Metric Categories: It should be possible to view that business model in terms of a number of metric categories such as the resources it consumes, the risks being run, the workload being performed, and the outcomes that are generated. Measures from these different categories will need to be displayed in combinations. For example, to show whether an activity is worthwhile requires its costs to be shown, along with the work performed and any outcomes. In addition, these metric views should be tailored to those people responsible for particular areas of the business.
Methodology support: It should adapt to an organisation’s chosen management methodology. i.e. it should conform with the terminology used and the way in which planning activities are prescribed.
Initiative management: It should allow the creation, selection, approval and monitoring of projects/strategic initiatives that improve organizational performance and how they link to corporate goals.
Scenario planning: It should allow combinations of initiatives to be assessed and the side-by-side analysis of alternate business models, through which senior management can set future plans.
Dynamic reports and analyses: It should communicate plans and results through personalised reports, analyses, dashboards, scorecards and strategy maps but in the context of how well the plan is being executed, so that the future can be better managed.
Dynamic workflow management: The system should be able to cope with continuous planning and monitoring of execution, which intelligently involves the right people at the right time, from across the enterprise.
Most people would agree that these capabilities are essential for managing strategy and its execution. Similarly, most CPM software vendors would claim to have these, but as they say, the devil is in the detail.
ACHIEVING ENERGY EFFICIENCIES IN COLD STORAGESJack Watson
Energy is the next most significant cost element in any cold storage after Manpower. Organizations globally are under pressure to reduce costs and be energy effi¬cient, while not compromising on service quality. This paper discusses the challenges in energy management for cold storages suggesting ways to collect and analyze energy and to arrive at energy saving strategies that when applied would help in cost, performance and energy efficiencies.
The document discusses various strategies for optimizing the energy efficiency of data centers, including:
1) Establishing an energy baseline and forecasting IT growth to determine optimization opportunities.
2) Implementing metrics like PUE and DCE to measure efficiency and compare to other data centers.
3) Improving airflow management through practices like hot/cold aisle layouts and blanking panels.
4) Matching cooling capacity to IT load and eliminating hot spots through technologies like modular cooling systems.
5) Considering alternative cooling technologies like carbon dioxide cooling that can reduce energy use by up to 30%.
This chapter introduces accounting information systems and discusses why they are important to study. It defines key terms like systems, data, and information. It explains that an AIS collects, records, stores and processes data to produce useful information for decision making. It also discusses how an AIS provides information that supports an organization's activities and value chain. Finally, it introduces the concept that an AIS should support an organization's overall business strategy, whether that is a strategy of product differentiation or low costs.
The document discusses an energy management system created by Gloabtel Convergence Ltd. to monitor and analyze energy consumption. The system collects data from digital meters installed at various locations, analyzes the electrical data in real-time, and provides reports to help users track energy usage, costs, and identify inefficiencies. The system offers benefits like reduced energy costs, improved power quality monitoring, and helps optimize processes to lower consumption.
A Big Data Approach to Energy Management in RetailJack Watson
Retail businesses spend billions of dollars each year on energy. The industry faces the dual challenge of not only reducing carbon footprint, but also imbibing sustainable strategies that balance business objectives with environmental responsibilities. This paper discusses how the effective usage of Big Data Analytics can revolutionize energy sustainability initiatives for retailers, driving consumption pattern analysis, establishing efficiency blueprints and supporting maintenance efforts.
The CorPeuM mission is to improve the execution of strategy!
The basis of all performance management is in administering an organization’s business activities (sales, marketing, production, product development, etc.) in an environment that is increasingly uncertain. As outlined in ‘What is Strategy Execution?’, a strategy execution system should support the way in which these business processes are planned and monitored. This will require a number of integrated application capabilities, including:
Business Modelling: The system should be able to model an organization’s current and proposed business processes that show how they are connected to achieve the organization’s purpose.
Metric Categories: It should be possible to view that business model in terms of a number of metric categories such as the resources it consumes, the risks being run, the workload being performed, and the outcomes that are generated. Measures from these different categories will need to be displayed in combinations. For example, to show whether an activity is worthwhile requires its costs to be shown, along with the work performed and any outcomes. In addition, these metric views should be tailored to those people responsible for particular areas of the business.
Methodology support: It should adapt to an organisation’s chosen management methodology. i.e. it should conform with the terminology used and the way in which planning activities are prescribed.
Initiative management: It should allow the creation, selection, approval and monitoring of projects/strategic initiatives that improve organizational performance and how they link to corporate goals.
Scenario planning: It should allow combinations of initiatives to be assessed and the side-by-side analysis of alternate business models, through which senior management can set future plans.
Dynamic reports and analyses: It should communicate plans and results through personalised reports, analyses, dashboards, scorecards and strategy maps but in the context of how well the plan is being executed, so that the future can be better managed.
Dynamic workflow management: The system should be able to cope with continuous planning and monitoring of execution, which intelligently involves the right people at the right time, from across the enterprise.
Most people would agree that these capabilities are essential for managing strategy and its execution. Similarly, most CPM software vendors would claim to have these, but as they say, the devil is in the detail.
ACHIEVING ENERGY EFFICIENCIES IN COLD STORAGESJack Watson
Energy is the next most significant cost element in any cold storage after Manpower. Organizations globally are under pressure to reduce costs and be energy effi¬cient, while not compromising on service quality. This paper discusses the challenges in energy management for cold storages suggesting ways to collect and analyze energy and to arrive at energy saving strategies that when applied would help in cost, performance and energy efficiencies.
The document discusses various strategies for optimizing the energy efficiency of data centers, including:
1) Establishing an energy baseline and forecasting IT growth to determine optimization opportunities.
2) Implementing metrics like PUE and DCE to measure efficiency and compare to other data centers.
3) Improving airflow management through practices like hot/cold aisle layouts and blanking panels.
4) Matching cooling capacity to IT load and eliminating hot spots through technologies like modular cooling systems.
5) Considering alternative cooling technologies like carbon dioxide cooling that can reduce energy use by up to 30%.
This chapter introduces accounting information systems and discusses why they are important to study. It defines key terms like systems, data, and information. It explains that an AIS collects, records, stores and processes data to produce useful information for decision making. It also discusses how an AIS provides information that supports an organization's activities and value chain. Finally, it introduces the concept that an AIS should support an organization's overall business strategy, whether that is a strategy of product differentiation or low costs.
The document discusses an energy management system created by Gloabtel Convergence Ltd. to monitor and analyze energy consumption. The system collects data from digital meters installed at various locations, analyzes the electrical data in real-time, and provides reports to help users track energy usage, costs, and identify inefficiencies. The system offers benefits like reduced energy costs, improved power quality monitoring, and helps optimize processes to lower consumption.
This document discusses accounting information systems and the role of accounting information. It covers four main types of accounting information: operating, financial, management, and tax accounting information. It also discusses the need for quantitative and non-quantitative information in accounting. Accounting information systems are used to collect, process, and report financial information to support decision making, planning, implementation, and control functions. The accountant plays an important role in designing, using, and auditing accounting information systems.
This chapter discusses accounting information systems and key related concepts. It addresses what an accounting information system is, why they are important to study, and how they provide information for decision making. The chapter also explores the concepts of systems, data, and information; the role of an AIS in an organization's value chain; and how an AIS's design is influenced by information technology, corporate strategy, and culture.
The document discusses enterprise asset management (EAM) and asset performance management (APM) solutions. It states that EAM focuses on documenting maintenance events while APM provides continuous insights to optimize asset performance using real-time data. The document then provides information on various solutions offered by Troia, including their monitoring platform, IT service management system, augmented reality applications, and tools that integrate various data sources to provide analytics and insights.
This document summarizes the results of a benchmark test of AMT-SYBEX's Affinity Meterflow smart meter data management software. The software was able to process 10 million meters' worth of data in under 30 minutes and 100 million meters' worth of data in under 7 hours. This demonstrated a ten-fold increase in performance over other solutions. The software validates, transforms, loads, analyzes, estimates missing values for, and bills for the large volumes of interval-based smart meter data.
The purpose of this paper is to examine the need for smart metering of facilities at the branch circuit level and
determine if such implementations would pay back their investment.
1. Accountants play several roles in information systems including users, designers, and auditors. As users, they must communicate their needs to systems designers. As designers, they are responsible for the conceptual system which determines information requirements and accounting rules. As auditors, they evaluate information systems internally and externally.
2. An accounting information system (AIS) processes financial and some non-financial transactions to produce financial statements and reports. It captures data from internal and external sources and transforms it into useful information through collection, processing, management, and generation functions.
3. A well-designed AIS provides relevant, timely, accurate, and complete information to support management decision making and day-to-day operations. It
A big-data-approach-to-energy-management-in-retailsiya4
Big data analytics can help retailers significantly reduce energy consumption and costs. By collecting and analyzing large amounts of data on energy usage from multiple stores, inefficiencies can be identified and addressed. This allows standardization of operations and identification of unexpected issues. Leading retailers have saved 4-20% on energy costs through big data-driven programs that monitor equipment usage and optimize performance. In addition to energy savings, big data can improve maintenance, planning, and customer experience for retailers.
A Big Data Approach to Energy Management in RetailNidhi Vora
Retail businesses spend billions of dollars each year on energy. The industry faces the dual challenge of not only reducing carbon footprint, but also imbibing sustainable strategies that balance business objectives with environmental responsibilities. This paper discusses how the effective usage of Big Data Analytics can revolutionize energy sustainability initiatives for retailers, driving consumption pattern analysis, establishing efficiency blueprints and supporting maintenance efforts.
A big-data-approach-to-energy-management-in-retailmalini87
Big data analytics can help retailers significantly reduce energy consumption and costs. By collecting and analyzing large amounts of data on energy usage from multiple stores, inefficiencies can be identified and addressed. This allows standardization of operations and identification of unexpected issues. Leading retailers have saved 4-20% on energy costs through big data-driven programs that monitor equipment usage and optimize performance. In addition to energy savings, big data can improve maintenance, sustainability reporting, project planning, and the customer experience.
This paper discusses how the effective usage of Big Data Analytics can revolutionize energy sustainability initiatives for retailers, driving consumption pattern analysis, establishing efficiency blueprints and supporting maintenance efforts. Indeed, Big Data is the harbinger of an efficient tomorrow, bolstering the retailer’s fiscal as well as competitive repute in the market, combined with the augmentation of savings potential.
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Cognizant
Predictive analytics is a process of using statistical and data mining techniques to analyze historic and current data sets, create rules and predict future events. This paper outlines a game plan for effective implementation of predictive analytics.
This document discusses how building analytics uses data from building management systems and Internet of Things devices to optimize building performance. It analyzes data on faults, opportunities, and capital projects to detect issues and inefficiencies. Measurement and verification is used to quantify energy savings. Managed services provide continuous remote monitoring, optimization, and maintenance to keep buildings running efficiently. BACnet is highlighted as an important open protocol that allows different building systems and analytics platforms to integrate and share data.
This document discusses how business analytics can help improve the Indian power sector. It explains that business analytics can help better manage the power sector to make it more financially viable and promote competition, in line with objectives of the Indian Electricity Act of 2003. The document outlines the role of integrating business analytics with big data in the power sector. It also discusses software requirements and attributes needed for business analytics in power generation, transmission and distribution utilities. Finally, it provides examples of how business analytics can help analyze customer data, transmission losses, revenue realization, plant efficiencies, load forecasting, and support faster decision making.
This document discusses how business analytics can help improve the management and financial viability of India's power sector. It describes how analytics can be applied across generation, transmission, and distribution to better understand operations, reduce losses, improve revenue, and enhance decision making. The integration of business analytics with big data from the power sector is seen as important for developing power quality services. Overall, business analytics has the potential to improve various aspects of the power sector in India through more effective analysis of the large amount of operational and customer data that is now available.
Meter Data Analytics-Deriving Maximum Value from Meter DataNidhi Vora
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data.
Meter data-analytics-deriving-maximum-value-from-meter-datamalini87
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data.
Customized Energy Solutions for Meter Data AnalyticsShreeja Sahadevan
Metered data can be used to identify usage patterns, sources of energy consumption and classi_cation of peak loads into critical and non-critical. This insight can then be used to determine if any of the peak loads can be shifted to non-peak hours or if non-critical loads be reduced. This helps in decreasing the peak load charges.
Meter data-analytics-deriving-maximum-value-from-meter-datasiya4
This document summarizes ways to derive maximum value from energy meter data, including benchmarking facilities, identifying base loads, seasonal patterns, load breakdown, forecasting, peak shaving, equipment efficiency, and more. Meter data can be analyzed to benchmark facilities, identify unjustified energy use during off-hours or non-working periods, understand seasonal impacts, predict future usage, reduce peak demand charges, check equipment performance, and more. Capturing granular usage data enables numerous insights that support energy savings.
Meter Data Analytics-Deriving Maximum Value from Meter Dataaayamkhatri
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data.
Meter Data Analytics: DERIVING MAXIMUM VALUE FROM METER DATAJack Watson
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data
Implementing Oracle Utility-Meter Data Management For Power ConsumptionIJERDJOURNAL
ABSTRACT: In this digital mobile world, it‟s need of time to streamline and increase efficiency in business processes like effective data collection, measurement, automatic validation, editing and estimation of measurement data, analysis and dashboard for forecasting and ease in end user accessibility with Just in Time. This paper is following two methodology in this process. CEMLI is an extensive framework for developing and implementing for Oracle whereas OUM is business process and use case driven process which supports products, tool, technologies and documentation. This paper have focused on analytical data, system automation functionality along with prototype designing. For this, analysts and administrators will collect and define calculation rule for data collection and measurement, deployment methods, dashboards and security features. This paper gives measure understanding of cloud technologies and their features like services (SaaS), deployment methods, security and ability to reduce overhead cost, downtime, and automate business processes with 360 degree review and analysis. It consolidates data in one system with volumes of analog and interval data which facilitates new customer with offering and effective program. Also it maximizes return on investments and protects revenue through comprehensive exception management.
Hotel facilities worldwide are under mounting pressure to do more with less - to provide superior guest experience while controlling costs and eliminating waste. This document describes how Wipro EcoEnergy’s Managed Energy Services Program (MESP) for the hospitality sector addresses these challenges and continuously delivers sustained savings.
This document discusses accounting information systems and the role of accounting information. It covers four main types of accounting information: operating, financial, management, and tax accounting information. It also discusses the need for quantitative and non-quantitative information in accounting. Accounting information systems are used to collect, process, and report financial information to support decision making, planning, implementation, and control functions. The accountant plays an important role in designing, using, and auditing accounting information systems.
This chapter discusses accounting information systems and key related concepts. It addresses what an accounting information system is, why they are important to study, and how they provide information for decision making. The chapter also explores the concepts of systems, data, and information; the role of an AIS in an organization's value chain; and how an AIS's design is influenced by information technology, corporate strategy, and culture.
The document discusses enterprise asset management (EAM) and asset performance management (APM) solutions. It states that EAM focuses on documenting maintenance events while APM provides continuous insights to optimize asset performance using real-time data. The document then provides information on various solutions offered by Troia, including their monitoring platform, IT service management system, augmented reality applications, and tools that integrate various data sources to provide analytics and insights.
This document summarizes the results of a benchmark test of AMT-SYBEX's Affinity Meterflow smart meter data management software. The software was able to process 10 million meters' worth of data in under 30 minutes and 100 million meters' worth of data in under 7 hours. This demonstrated a ten-fold increase in performance over other solutions. The software validates, transforms, loads, analyzes, estimates missing values for, and bills for the large volumes of interval-based smart meter data.
The purpose of this paper is to examine the need for smart metering of facilities at the branch circuit level and
determine if such implementations would pay back their investment.
1. Accountants play several roles in information systems including users, designers, and auditors. As users, they must communicate their needs to systems designers. As designers, they are responsible for the conceptual system which determines information requirements and accounting rules. As auditors, they evaluate information systems internally and externally.
2. An accounting information system (AIS) processes financial and some non-financial transactions to produce financial statements and reports. It captures data from internal and external sources and transforms it into useful information through collection, processing, management, and generation functions.
3. A well-designed AIS provides relevant, timely, accurate, and complete information to support management decision making and day-to-day operations. It
A big-data-approach-to-energy-management-in-retailsiya4
Big data analytics can help retailers significantly reduce energy consumption and costs. By collecting and analyzing large amounts of data on energy usage from multiple stores, inefficiencies can be identified and addressed. This allows standardization of operations and identification of unexpected issues. Leading retailers have saved 4-20% on energy costs through big data-driven programs that monitor equipment usage and optimize performance. In addition to energy savings, big data can improve maintenance, planning, and customer experience for retailers.
A Big Data Approach to Energy Management in RetailNidhi Vora
Retail businesses spend billions of dollars each year on energy. The industry faces the dual challenge of not only reducing carbon footprint, but also imbibing sustainable strategies that balance business objectives with environmental responsibilities. This paper discusses how the effective usage of Big Data Analytics can revolutionize energy sustainability initiatives for retailers, driving consumption pattern analysis, establishing efficiency blueprints and supporting maintenance efforts.
A big-data-approach-to-energy-management-in-retailmalini87
Big data analytics can help retailers significantly reduce energy consumption and costs. By collecting and analyzing large amounts of data on energy usage from multiple stores, inefficiencies can be identified and addressed. This allows standardization of operations and identification of unexpected issues. Leading retailers have saved 4-20% on energy costs through big data-driven programs that monitor equipment usage and optimize performance. In addition to energy savings, big data can improve maintenance, sustainability reporting, project planning, and the customer experience.
This paper discusses how the effective usage of Big Data Analytics can revolutionize energy sustainability initiatives for retailers, driving consumption pattern analysis, establishing efficiency blueprints and supporting maintenance efforts. Indeed, Big Data is the harbinger of an efficient tomorrow, bolstering the retailer’s fiscal as well as competitive repute in the market, combined with the augmentation of savings potential.
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Cognizant
Predictive analytics is a process of using statistical and data mining techniques to analyze historic and current data sets, create rules and predict future events. This paper outlines a game plan for effective implementation of predictive analytics.
This document discusses how building analytics uses data from building management systems and Internet of Things devices to optimize building performance. It analyzes data on faults, opportunities, and capital projects to detect issues and inefficiencies. Measurement and verification is used to quantify energy savings. Managed services provide continuous remote monitoring, optimization, and maintenance to keep buildings running efficiently. BACnet is highlighted as an important open protocol that allows different building systems and analytics platforms to integrate and share data.
This document discusses how business analytics can help improve the Indian power sector. It explains that business analytics can help better manage the power sector to make it more financially viable and promote competition, in line with objectives of the Indian Electricity Act of 2003. The document outlines the role of integrating business analytics with big data in the power sector. It also discusses software requirements and attributes needed for business analytics in power generation, transmission and distribution utilities. Finally, it provides examples of how business analytics can help analyze customer data, transmission losses, revenue realization, plant efficiencies, load forecasting, and support faster decision making.
This document discusses how business analytics can help improve the management and financial viability of India's power sector. It describes how analytics can be applied across generation, transmission, and distribution to better understand operations, reduce losses, improve revenue, and enhance decision making. The integration of business analytics with big data from the power sector is seen as important for developing power quality services. Overall, business analytics has the potential to improve various aspects of the power sector in India through more effective analysis of the large amount of operational and customer data that is now available.
Meter Data Analytics-Deriving Maximum Value from Meter DataNidhi Vora
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data.
Meter data-analytics-deriving-maximum-value-from-meter-datamalini87
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data.
Customized Energy Solutions for Meter Data AnalyticsShreeja Sahadevan
Metered data can be used to identify usage patterns, sources of energy consumption and classi_cation of peak loads into critical and non-critical. This insight can then be used to determine if any of the peak loads can be shifted to non-peak hours or if non-critical loads be reduced. This helps in decreasing the peak load charges.
Meter data-analytics-deriving-maximum-value-from-meter-datasiya4
This document summarizes ways to derive maximum value from energy meter data, including benchmarking facilities, identifying base loads, seasonal patterns, load breakdown, forecasting, peak shaving, equipment efficiency, and more. Meter data can be analyzed to benchmark facilities, identify unjustified energy use during off-hours or non-working periods, understand seasonal impacts, predict future usage, reduce peak demand charges, check equipment performance, and more. Capturing granular usage data enables numerous insights that support energy savings.
Meter Data Analytics-Deriving Maximum Value from Meter Dataaayamkhatri
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data.
Meter Data Analytics: DERIVING MAXIMUM VALUE FROM METER DATAJack Watson
A successful metering strategy requires more than installing the meters. A metered data gives a direct view of energy consumption at each of the facilities; it also acts as the fundamental piece of information in computing appropriate efficiency metrics. This article explains how to derive maximum value out of metered data
Implementing Oracle Utility-Meter Data Management For Power ConsumptionIJERDJOURNAL
ABSTRACT: In this digital mobile world, it‟s need of time to streamline and increase efficiency in business processes like effective data collection, measurement, automatic validation, editing and estimation of measurement data, analysis and dashboard for forecasting and ease in end user accessibility with Just in Time. This paper is following two methodology in this process. CEMLI is an extensive framework for developing and implementing for Oracle whereas OUM is business process and use case driven process which supports products, tool, technologies and documentation. This paper have focused on analytical data, system automation functionality along with prototype designing. For this, analysts and administrators will collect and define calculation rule for data collection and measurement, deployment methods, dashboards and security features. This paper gives measure understanding of cloud technologies and their features like services (SaaS), deployment methods, security and ability to reduce overhead cost, downtime, and automate business processes with 360 degree review and analysis. It consolidates data in one system with volumes of analog and interval data which facilitates new customer with offering and effective program. Also it maximizes return on investments and protects revenue through comprehensive exception management.
Hotel facilities worldwide are under mounting pressure to do more with less - to provide superior guest experience while controlling costs and eliminating waste. This document describes how Wipro EcoEnergy’s Managed Energy Services Program (MESP) for the hospitality sector addresses these challenges and continuously delivers sustained savings.
Hotel facilities worldwide are under mounting pressure to do more with less - to provide superior guest experience while controlling costs and eliminating waste. This document describes how Wipro EcoEnergy’s Managed Energy Services Program (MESP) for the hospitality sector addresses these challenges and continuously delivers sustained savings.
Hotel facilities worldwide are under mounting pressure to do more with less - to provide superior guest experience while controlling costs and eliminating waste. This document describes how Wipro EcoEnergy’s Managed Energy Services Program (MESP) for the hospitality sector addresses these challenges and continuously delivers sustained savings.
Hotel facilities worldwide are under mounting pressure to do more with less - to provide superior guest experience while controlling costs and eliminating waste. This document describes how Wipro EcoEnergy’s Managed Energy Services Program (MESP) for the hospitality sector addresses these challenges and continuously delivers sustained savings.
Hotel facilities worldwide are under mounting pressure to do more with less - to provide superior guest experience while controlling costs and eliminating waste. This document describes how Wipro EcoEnergy’s Managed Energy Services Program (MESP) for the hospitality sector addresses these challenges and continuously delivers sustained savings.
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Using Big Data for Operations & Energy Management in Hospitality
1. USING BIG DATA FOR OPERATIONS &
ENERGY MANAGEMENT IN HOSPITALITY
www.wiproecoenergy.com
ANALYZE. ACHIEVE. ACCELERATE
2. Power of Big Data Analytics
06
05
Table of Content
03 .................................................................................... Abstract
04 .................................................................................... Need for Operational & Energy Efficiency
04 .................................................................................... Breaking the Silos
....................................................................................
.................................................................................... Journey In Advanced Analytics
06 .................................................................................... ‘Center’ of Gravity
07 .................................................................................... About the Authors
3. The hospitality industry is increasingly focusing on
better energy management as it looks to improve
guest comfort levels while optimizing energy
consumption. The effort is to reduce carbon
footprint, bring in more energy efficiency by use of
data-driven approaches and keep operational costs
under check.
The industry,amid its diversity,faces certain common
concerns about areas in which energy is wasted. Its
03
Abstract
ANALYZE. ACHIEVE. ACCELERATE
diversity lies in the different kind of establishments it
supports – be it large convention hotels, restaurants,
guest houses,inns or motels.The varied nature of the
physical facilities and the activities that they host can
make energy management especially challenging.
This paper talks about the data analytics-based
approach to determine Operational Effectiveness
and Energy Management (OE&EM) for the
hospitality industry.
4. Unlike other commercial buildings, hotels have unique energy
requirements because of the variety of facilities available, functions
provided and operational schedules. A hotel usually operates 24X7,
round the year, although some sections such as the ballroom or a
restaurant may be closed during specific periods in a day. Guest
occupancy levels in hotels vary significantly across the year. In many
hotels, even when a guestroom is not occupied, the air conditioning is
kept switched on to prevent odor or guest discomfort. These factors
contribute to the rising need for a specialized focus on Energy
Management and Operational Effectiveness in the hospitality industry.
Advancements in IT, data convergence, and data analytics have found
increased relevance in the way facility operations and management
services are being delivered in the industry. Key Performance
Indicators (KPIs) such as guest comfort, complaints, internal thermal
and schedule policy compliance, energy consumption, benchmarking,
etc. are being examined by hotel operators closely to understand and
answer the “whats” and “whys,” resulting in better insights into energy
consumption and operations.
Need for Operational
& Energy Efficiency
The hospitality industry has Facility Management Systems and Energy
Management Systems such as Building Management System (BMS),
guest room controls system, metering/sub-metering and Property
Management Systems working in silos. The recent Innovations in
Information Communication Technology and high performance
machines saw advent of concepts such as cloud connectivity, high speed
data transfer, distributed databases, open standards for building
controls such as LONWorks, BACnet, OPC, etc.
These allow the hospitality sector to integrate various energy
management systems and use the data for informed decision making in
day-to-day operations that can help improve:
• Guest comfort
• Overall hotel energy efficiency
• Lifecycle operating cost of hotel assets
Breaking the Silos
Energy Operations Management
Systems and Uses
04
Building controls systems are an offspring of industrial automation and
controls systems. These systems are proprietary in nature and were
conceived for use in standalone mode, with limited operational and
consumption data sharing capabilities which can be leveraged for
analytical purposes.
The end of 20th century saw standards like TCP/IP, XML and standard
databases being acknowledged as the need for enhancing the scope of
data sharing for remote monitoring, centralized aggregation, analytics
etc. These technologies form the foundation for development of
enterprise data analysis systems by collecting data from multiple
sources such as Building Management System or Control Systems,
Energy Monitoring System, Guest Room Management System and
Property Management System (see Figure 1). They provide insights we
wouldn’t otherwise have.
BMS companies are lagging in adoption of the required IT standards.
There is a major challenge in integrating with analytical engine for
operational improvement and energy cost reduction.
Property Management System: System used as Guest
database for booking, special instructions (VIP room, etc.),
also sometimes provided integration with guest complains
Building Management System: Control system
controlling and Connecting common area assets such
AHUs, Pumps, Chillers
Guest Room Controls: Smart thermostats controlling the
lighting, HVAC equipment in hotel rooms. Some hotels still have
old thermostats which do not have commutating capabilities
Energy Monitoring System: Hospitality industry has
many meters (mostly standalone but having commutating
capabilities) installed
5. 05
ANALYZE. ACHIEVE. ACCELERATE
The hospitality industry can effectively reduce energy use by leveraging
on the OE&EM Platform as this helps improve bottom line and
brings down operating costs. Understanding energy and operations
components (such as energy and operations spend and it’s break-up, its
correlation with business needs, guest comfort, weather, availability,
etc.) in the sector is the first step towards this.
Energy and operations management can be achieved by ensuring major
systems are run as per best optimized schedule and set points,
detecting break down, identifying inefficiency of systems, and reducing
Contemporary Building Management Systems (also called as Energy
Management System) provide capabilities for collecting millions of data
points, deviation detection, data trends, schedule management and
consumption monitoring. However, there is an abundance of data and an
absence of analysis, making it a “data rich but information poor” situation.
Power of Big Data Analytics
energy leaks. This is done by integrating BMS, utility meters and
sub-meters, guest room controls, Property Management System and
other business systems (such as financial components related to
breakdown cost, Annual Maintenance Contract, retrofit costs, service
costs, work orders, etc.) to collect site data on a central energy
platform. The OE&EM Platform has the capability to run algorithms.
Data analysts and scientists use it to identify opportunities for
operational improvements and energy cost reductions.
Big Data analytics tools helps identify “What” (pattern analysis) as
reference and later Subject Matter Experts use this pattern analysis to
identify the “Whys” and action to correct the deviation. This
methodology of using analytics to transform data into useful information
is the added advantage of OE&EM Platform-based operations.
MIS Reports,
Scenario Panning,
SLA tracking, KPI
Management
Dashboards
Onsite Support
Multiple Facilities
External Data Feeds
Other Data Sources
Primary Data
Sources; Installed at
Multiple Locations
in Hotels
Offshore
Analytics Desk
Central
Data
Storage
Platform for Operations Effectiveness & Energy Management (OE & EM)
ControlBack*
CentralizedDataCenter
PacificTime
ZoneData
Aggregator
Data Integrator
and Link
Concentrators
Room Controls
with PMS
Metering /
Sub-Metering ChillersAHUs
Data Integrator
and Link
Concentrators
Data Integrator
and Link
Concentrators
MountainTime
ZoneData
Aggregator
CentralTime
ZoneData
Aggregator
EasternTime
ZoneData
Aggregator
An OE&EM Platform
6. 06
Journey In Advanced Analytics
‘Center’ of Gravity
This framework will help make the Energy Management System of the hospitality industry a more data-centric one which will boost operational
efficiencies with minimum capital investments. Being a continuous process, it also helps sustain the operational improvements and energy savings for
a longer period. The centralized view enables organizations to compare their performances through benchmarking and by prioritizing action plans.
An analytics-based approach, backed by a energy and operations center, which is different from the traditional approach of retrofits and
refurbishments, is the ideal way to move forward. Key elements of analytics-based Operational Effectiveness and Energy Management approach are:
• It is a No/Low CAPEX model for achieving energy savings that leverage existing infrastructure
• Empowers operations and management teams to proactively manage equipment performance and reduce downtime. It reduce man-hours spent
on diagnosis and reporting
The goal of OE&EM is to generate Business Intelligence. Typical value creation process based on data analytics can be depicted
Value
Creation
Process
KPIs:Define and track
metrics related to OE&EM
Advance Data Analytics
Action: Plan corrective actions,
track changes; refine KPIs
Value Creation Process Cycle
• For accurate data
collection, we need
to define
(i) When and how often to
collect data on energy, cost,
weather etc.
(ii) Units of measurement
(iii) Managing data duplication
• Data Cleaning – is used to
remove irrelevant data
• Standard reports – that help
understand what deviations
happened and why. It can be a
monthly or quarterly report
• Regression Analysis- Quantifies relationship
between the dependent, i.e, energy use, and
independent variables such as weather,
occupancy, etc
• Specific guidelines on data
storage devices, permissions
to read/write the data, data
structure algorithms,
periodic maintenance, data
warehousing, etc.
• Data Integration –
Extract, Transform and
Load (ETL) processes can
be used to collate data
from outside sources,
transform into
operational needs, load it
into end target
• Data Selection – data
relevant for the analysis is
extracted using
algorithms and tools like
SQL, Access, Excel, etc.
• Ad hoc/operational and energy
performance deviation reports –
that help understand how many
deviations, how often, where, etc.
• Query drilldown/Root cause
analysis – helps understand key
issues such as major energy
usuage deviations, schedule and
thermal policy violations, where
exactly is the problem, or how
can it be rectified
• Simple statistical tests like T Test,
Z Test can be used in some cases
• Correlation Analysis – to know the
relationship between two variables such as
temperature and consumption
• Cluster Analysis - to find homogenous
groups across sites
• Forecasting - helps pre-plan through
series of variables related to seasonality
and weather
• Predictive Modeling – helps prepare
contingency plans
• Optimization – helps plan how to do
things better
Data Collection
and Storage
Data
Preparation
Data
Analysis
Advanced
Analytics
7. 07
ANALYZE. ACHIEVE. ACCELERATE
Ankur Thareja is a Senior Consultant – Energy Solutions at Wipro EcoEnergy. With a career span of almost 13 years,
focusing on delivering solutions for energy and controls, Ankur’s areas of expertise include Integrated Building Management
Systems, Energy Management Solutions, Remote Monitoring, Performance Contracting and Intelligent/Smart Buildings.
He is Certified Energy Auditor (CEA) from the Bureau of Energy Efficiency (BEE), Certified Measurement and Verification
Professional (CMVP) by Efficiency Valuation Organization (EVO) and an Accredited Professional from Indian Green Building
Congress (IGBC).
Thanakarthik Kumar Karuppasamy, a Consultant for Energy Solutions at Wipro EcoEnergy, has over
10 years of experience. He has been working in areas of energy management solutions, energy audit for buildings and
industries. He holds a bachelor degree in Electrical and Electronics Engineering. He is a Certified Energy Auditor
(CEA) from the Bureau of Energy Efficiency (BEE), Certified Measurement and Verification Professional (CMVP)
by Efficiency Valuation Organization (EVO) and an Energy Management System (ISO 50001) lead auditor.
About the Authors