The best energy decisions start with monitoring energy behind the meter, or demand side monitoring, but many facility owners do not collect or retain their own energy information.
Even a brief historical platform can go a long way towards establishing the essential relationship between the key performance indicators (KPIs) that drive energy cost like kWh per unit production, or energy cost to store 1000 SKUs per day, etc.
To perform the analysis, specific and discrete data on real-time energy costs against unit production is needed. PredictEnergy® creates an “Energy Cube.” The energy cube is a multi dimensional data view of a facility’s energy performance profile based on the following information:
Energy Decision Metrics
- Energy and Power by date, time and location
- Unit production or distribution volume by date, time and location
- Utility Tariff Rate
Energy reduction management programs are well known, such as; load shifting, load sharing, demand response, renewable energy production, energy efficiency measures and tariff adjustments to name a few. But which is best? Understanding how to use the information to determine the best energy program by predicting best results to maximize savings is the real challenge.
To accomplish the best program, the Energy Cube is used to provide a complete picture of the energy cost to make, move, or store your products and how to reduce those costs. The resultant Energy Cube shows a vivid profile of the principal drivers to the total cost of energy, and sets up an evaluation of “What if” energy reduction scenarios that are iterated for best results. Typical energy reduction scenarios might include process modifications, on-site generation, tariff rate changes, load management, recommissioning and others. The energy analytics breakthrough in PredictEnergy™ is in determining the “difference that makes a difference.” In other words, energy analytics uses the Energy Cube to determine which scenarios to analyze to determine the best energy cost reduction results.