Data-Driven Energy Management: a New Approach to Efficiency5 min read
Infrastructure upgrades can only do so much. To execute on energy management, you need a strong data foundation.
Energy supply chain disruptions, skyrocketing costs, and shifting environmental regulations have made reducing your energy use a business-critical objective. But these same dynamics also mean that the goalposts for energy efficiency are constantly shifting. Can your energy management strategy bring down your consumption today while also preparing you for what’s ahead?
Achieving energy efficiency is a continuous improvement process requiring careful navigation, and no company’s process will be the same. The key is making sure that each individual improvement produces value.
That won’t be easy. Your company is faced with legacy systems and unpredictable external conditions. And traditional analytic methods cannot model the factors impacting energy consumption — they are simply too numerous and complex.
To unlock new opportunities for energy efficiency, you need to embrace IT as a business partner and boost your data capabilities.
Infrastructure updates, diminishing returns, and silos
We’ve all sought to bring down our energy use by replacing outdated technologies with modern infrastructure — why use a gas heater when efficient heat pumps are readily available? But that will only take you so far, because ad-hoc spending on infrastructure always produces diminishing returns.
Energy management is not off-the-shelf.
The reason is simple: Isolated interventions produce silos. While each upgrade marginally improves efficiency, it will not provide you with insight into how to reduce energy use elsewhere. If your infrastructure improvements are not talking to each other, they cannot position you for further efficiency gains.
Foundation and execution
To avoid the pitfalls of data silos and diminishing returns, you need to develop a long-term technological roadmap.
For that, you require a robust and future-proof unified data model to ingest and centralize the data generated in disparate parts of your services and systems — breaking down digital and organizational silos. That lets you contextualize, combine, correlate, and continuously analyze your data across all sources. You can store your data in the cloud and migrate it across clouds — all integrated with third-party technologies for rapid consumption and cost-effective development.
With a robust data foundation, you unlock an executional architecture to manage your energy use.
By creating a data foundation, you position your firm to secure the enduring efficiency gains from the technologies contained in the executional level, such as interactive dashboards to monitor and report energy efficiency, forecasting models, simulation models, and optimization models.
Compounding value and efficiency
Reaching the executional level is not a leap — it is a result of an evolution through five phases.
Just three steps to deep, predictive insights — five steps to self-optimizing efficiency.
These five phases progress with increasing complexity and value. By comparing your current maturity with your goals, you can focus on the phase that will deliver the most immediate ROI.
Each stage also positions you to integrate additional services for more energy savings. For instance, dashboards already assist your plant managers by bringing visibility to data, but you can enhance them with forecasting and simulation models of “what-if” scenarios to go beyond historical data.
Grow your maturity and your efficiency with each phase.
That kind of analysis would be unthinkable without deep data insights. But the possibilities do not end there. Once you have reached the highest level of data maturity, you can deploy fully automated, self-optimizing control of physical processes with artificial intelligence at its core.
New opportunities through AI
Recently, our client, a premium German car manufacturer, aimed for nothing less than that. After identifying energy inefficiencies in their painting operations, they realized that traditional modeling and calibration could not handle the factors at play.
To relate airflow characteristics (temperature and humidity) to physical appliances control (valve status, rpms), and energy consumption, they implemented a machine learning-based solution and created a high-fidelity simulation model. They then deployed a reinforcement learning environment to generate strategies to reduce energy consumption and guarantee zero deviations from air quality specifications.
There is no one-size-fits-all solution to energy management.
Now, they can rapidly adapt to new weather patterns and operational parameters, minimize their energy consumption, and easily scale the solution to other plants.
The appeal of that level of energy efficiency is obvious, but most companies don’t know how to get themselves there. The road is long and lined with opportunities for failure that can derail even the strongest companies.
Luckily, you are not alone. At SoftServe, we collaborate with our clients to help them to discover and build upon the energy-saving potential in their own infrastructure.
So, if you are ready to take the first step towards data maturity and efficient energy use, let’s talk.
Together, we can build a greener, more efficient tomorrow.
And if you want to learn even more about data-driven energy management, read our white paper.