Operations and production managers are challenged to run their energy-intensive production environments to optimize energy costs while producing outputs that meet the required specifications and timeline. The lack of visibility of the energy use of each element of their plant and machinery estate means that they cannot take full advantage of all the aspects of their energy mix, which creates excessive business costs and an unnecessary carbon footprint.
The Upkip AI Energy Optimization Module provides a step-by-step framework for accurately estimating your energy use. It allows you to adapt your consumption model to optimize your energy consumption, utilizing the ideal energy mix based on business need, availability, and cost. It provides a roadmap that allows managers to plan and adapt their energy consumption as their needs change over time.
Collect data for energy
consumption using sensors
Retrieve energy pricing
information from geolocation
Fast/efficient, fast vs better optimization, number of hours to shift
Retrieve planning for next day. Plan automatically or manually.
The module uses Machine Learning to assess consumption patterns based on historical readings across the enterprise, at each location, and also down to the individual machine level. It can apply data analytics to understand when a particular machine is active, idle, at a stop, or switched off to understand its impact on power consumption throughout the working day and year.
It can apply data analytics to understand when a particular machine is active, idle, at a stop, or switched off to understand its impact on power consumption throughout the working day and year.
The module captures data from IoT sensors in your production environment to collect power consumption metrics that help you understand what a piece of plant or machinery uses throughout its daily usage.
The first step is to take the next day’s production plan to see what energy inputs will be needed to generate the planned outputs. Next, you configure the energy consumption optimization process to know where you can begin to make changes, whether you use a fast vs. an efficient production run or a fast vs. an optimized production run, given the scale of available workforce and their shifts, for example.
The first step is for the module to capture pricing information for the range of energy sources available to you, based on the location of your plant and machinery, across the enterprise. It can consolidate information about the costs for different energy types, depending on the time of day or year. It uses AI capabilities to simulate the power available from wind and solar sources, based on weather patterns and the available output to the market.
Our operators will contact you within 48 hours of you reaching out.
