What are Energy Analytics?
Energy analytics synthesize three distinct but inter-related pieces of information:
- How much energy and power an enterprise uses, and when and where it’s used.
- What an enterprise makes, moves or stores; where it performs this work, when it performs this work, and how this work changes and scales.
- What an enterprise pays, and how it procures, energy and power.
The simplistic approach to energy cost reduction focuses on basic energy efficiency and limited demand reduction techniques and projects. This is usually a good place to start, however complex energy environments working off complicated tariff rate structures require a more sophisticated approach.
Many energy cost reduction efforts fail not because the projects are performed badly, but because they neglect to take into account 2 crucial factors:
- Energy use and generation must support the business and scale as business conditions change; and,
- Energy cost reduction is not necessarily directly related to energy usage reduction.
The second point is not always obvious, and often requires explanation. But, depending upon the tariff, reducing load may not save money, and simply shifting load to the wrong time can be expensive. Often, business and operating conditions don’t offer a lot of flexibility, and energy analytics can help chart a path.
Energy Analytics Approach
The application of energy analytics starts with defining the key performance indicators (for example, kWh per unit produced, or cost to store 1000 SKUs for 1 day, or energy intensity per unit of flow, etc) that drive energy costs. The next step is to track energy costs in real-time, and create what HelioPower calls the “Energy Cube.” The Energy Cube is a three dimensional map of the following information:
- Energy and Power by date, time and location
- Production or distribution volume by date, time and location
Data acquisition and management of energy usage, production information and tariff rate parameters are combined for analysis by HelioPower’s patent-pending PredictEnergy® software analytics engine. The resultant Energy Cube shows a vivid profile of the principal drivers to the total cost of energy, and sets up an evaluation of hypothetical energy reduction scenarios that are iterated for best results. Typical energy reduction scenarios might include process modifications, on-site generation, tariff rate changes, large-load management, recommissioning and others. Energy analytics breakthroughs occur when determining the “difference that makes a difference.” In other words, energy analytics can help determine which scenario to work on first based upon the biggest impact.
Energy Analytics Example
Recall that the essential pieces of information needed to reduce energy bills behind the meter are your facility's sources and uses of energy and power, the enterprise's multi-level business metrics (KPIs) and any and all utility tariffs that apply. There really are no shortcuts. HelioPower has performed numerous energy analytics engagements and deployments of PredictEnergy® at Manufacturing Plants, Logistics Centers, and Water-Wastewater Facilities with quantified savings that reduced total operational energy costs by more than 20%. Energy analytics software like PredictEnergy® can not only forecast energy project savings and verify the results, but also identify additional savings in facility operations, tariff rates and technology opportunities. The application of advanced analytics for energy cost reduction enables Water-Wastewater facilities to reduce energy costs well beyond the savings typically gained using standard energy efficiency and load reduction techniques.