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, 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.
How can you optimize your energy savings?
Many energy cost reduction efforts fail 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. Business and operating conditions don’t offer a lot of flexibility. Depending on tariff rates, reducing load may not save money and shifting load to the wrong time can be expensive. But, energy analytics can help chart a path.
Learn More about Each Energy Analytics Area
Energy Analytics Approach
Energy analytics starts with defining 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.”
What is 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 the PredictEnergy® analytics engine. The energy cube shows a vivid profile of the principal drivers to the total cost of energy, setting 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 essential pieces of information needed to reduce energy bills are your facility's sources and uses of energy and power. This also includes your multi-level business metrics (KPIs) and utility tariffs that apply. HelioPower has deployed PredictEnergy® at manufacturing plants, logistics centers, and water-wastewater facilities with quantified savings that reduced total operational energy costs by more than 20%. PredictEnergy® not only forecasts project savings and verify results, but identifies additional savings in facility operations, tariff rates and technology opportunities. The application of advanced analytics for energy cost reduction enables facilities to reduce costs well beyond what's typically gained using standard energy efficiency and load reduction techniques.