Wasted energy is lost profit. Yet many companies struggle with effective energy management, let alone intelligent.
It requires collaboration between stakeholders across the company. It demands a holistic approach. It requires enterprise-wide management of information, control, monitoring and actionable analysis.
Intelligent energy management unites departments, platforms and providers.
In our view, there are 5 parts to the intelligent energy management approach beyond the core benefits of generating revenues & savings from stackable energy opportunities.
Demand response is a given. As are savings, trading and peak avoidance. That’s not to say they’re not important – they are the cornerstone of our technology and provide valuable financial benefits to our clients. But by limiting a business to just these opportunities and using the machine learning technology for only these purposes seems, for lack of a better description, almost wasteful.
So, how can your business implement an intelligent energy management approach?
Every energy strategy starts with metering, monitoring and control.
The intelligent part is using this monitoring for something worthwhile. Many energy managers have information at their fingertips, but need meaningful analysis to make sense of it all. Connecting to the machine-learning platform, optimisation can be automated and performance visibility is maximised through the online dashboard, creating actionable output.
This is the foundation of any energy performance improvement strategy. Comparing the measured performance of an asset, process or facility against others.
The intelligent part is benchmarking against anonymised data in your industry, while mapping patterns in your own energy consumption. This means intelligence and insights on every aspect of energy usage. This helps to flag up anomalies and trends and even offer guidance on wider carbon reduction and energy saving strategies.
3. Production Optimisation
We’ve worked with clients where their energy patterns tell us everything about their operations, productivity and OEE. Working out how to ensure maximum output for minimum energy use and cost can be tricky. The intelligent part comes with the integration of energy profiles, asset constraints and operational schedules to deliver more efficient asset deployment, which ties nicely into our next point…
4. Maintenance Strategy
Not many people are aware that the health of machinery can be determined by the energy it uses. Machinery is continually degrading, so by ascertaining the likelihood of a fault occurring, businesses can save significantly.
From our extensive research, we’ve found that a fault detected early results in approximately £8k in maintenance costs and production loss, whereas reactive maintenance can cost around £48k. That’s a 500% difference in cost.
The intelligent part is that, as mentioned in point 2, we can take the anomaly tracking and combine that with data on the site/machine specifics (cue our expert engineers) to build ad-hoc prognosis and maintenance models for each of your assets. We’ll also use the information from Product Optimisation (point 3) to determine the best time for maintenance, to avoid any disruption to operations.
Energy buyers & managers need to make informed decisions as their budget is often in millions. They need a market view over the horizon to define their yearly strategy and on a more regular basis, this strategy needs to be adapted to the fluctuating markets and the flexibility on-site.
The intelligent part is automatically updated market reports, live short and long term market prices, market fundamentals and calculated forward curve and smart tools for trend analysis. All of which can, if chosen, automate participation in energy markets and are underpinned by a Value at Risk (VaR) model.