数据驱动的能源优化

INTRODUCTION You have seen how recent shifts in the energy market have changed the competitivelandscape in manufacturing. Ongoing geopolitical events have sent costs skyrocketingworldwide and meeting governmental carbon-reduction regulations and sustainabilitygoals have become a requirement to secure new lines of credit. The imperative to gaincontrol over energy use has never been clearer. Agility will be necessary to reduce your total consumption, minimize costs, and secure yourenergy supply. Each step towards energy efficiency must contribute to subsequentinitiatives by growing your company’s understanding of — and control over — its energymanagement goals, strategies, and capabilities. The key to tapping into that virtuous cycle means understanding that truly effective energymanagement lies in using data to create a holistic strategy capable of self-correction, andthe integration of new technologies. Deploying data for energy management thus dovetailswith the industry-wide trajectory of establishing growing levels of data maturity across alloperations. This paper lays out the principles of data-driven energy management using the method ofcontinuous improvement. By learning from your past efforts to define future steps, thesestrategies can be easily introduced at any stage of your organization’s energy managementjourney. And with this knowledge, you will be able to create new energy-saving techniquesbased on a data-centric, proactive approach. WHAT IS INDUSTRIAL ENERGY MANAGEMENT ANDWHY IS IT ESSENTIAL? Energy management encompasses both the planning and operation of energy productionand energy consumption, as well as energy distribution and storage.It prescribesproactivity with regard to both procurement as well as distribution, and actively takes botheconomic and environmental aims into account. Companies like yours need to decide upontheir energy management goals based on their energy objectives, which can include: INNOVATION: COMPLIANCE: MARGINS AND RISKS: SUSTAINABILITY: •Comply with localregulations toavoid penalties•Fulfill governancerequirementsand ISO 50001standards•Gain access tocompliance-contingent funds •Reduce andmanage energycosts and risks•Optimize energyefficiency toincrease resiliency•Deploy by-products togenerate low-costenergy •Accelerate thetransition torenewable energyuse•Eliminatewaste productsand negativeexternalities•Foster a positiveESG reputationto establish newrevenue streams •Create innovativebusiness models,products, andservices•Achievecompetitiveadvantages witha cost-savinginfrastructure•Boost revenueby licensingout pioneeringtechnologies Effective energy management is rooted in the comprehensive analysis of the people,processes, and technologies involved in energy use. While this paper focuses on data andtechnology, it shows how the role of people and processes can be enhanced and reinforcedthrough data-based insights. Increasingly mature levels of data empower workers andimprove processes by showing which courses of action work and which do not — anchoringenergy management in a continuous improvement process. COMPOUNDING DATA MATURITY FOR ROBUSTENERGY MANAGEMENT Adata-based energy management strategy overcomes the problems created by ad-hocsolutions. Ad-hoc spending always produces diminishing returns; upgrades oninfrastructure, for example, can improve efficiency but entail high capital expenditures andwill, eventually, need to be replaced as technology improves. But that is not the mostcritical concern: Without reliable data-based insights, it is impossible to evaluate initiativesbefore or after implementation. To understand why, consider the challenges facing a company seeking to optimize energyuse in commercial buildings. Using only a general idea of a given structure's aggregateenergy consumption, the company has only minimal insight into the factors contributing toenergy use. Nor does it know the parameters for which energy consumption needs to beoptimized. Without visibility into those dynamics, decision makers cannot know to whatdegree their upgrades have produced results — much less how cost-effective they are. By enabling access to the dynamics of energy use, data is the essential condition of settingup an effective energy management strategy. But it does not suffice to use data in anerratic fashion, targeting only isolated areas of production without a view to deepening andexpanding data-based insights. That only serves to create data silos, which will hamper anysubsequent energy management efforts. The right approach is to develop increasinglymature data capabilities based on a flexible data infrastructure — anticipating andovercoming data silos for a comprehensive approach and actionable insights acrossoperations. Moving up the data maturity curve unlocks more advanced energy management initiativesfrom monitoring to analysis and forecasting, all the way to data-based prescriptions andautomated adaptation. THE COMPONENTS OF DATA-DRIVEN ENER