Historical Energy Data and Accuracy of Analysis and Projections

Energy ReportingBy: Hugo Vargas

A frequent question constantly arises around the accuracy of energy data estimations and the quality, in terms of size and completeness, of the data set that is used to create an energy baseline. For those of you who don’t know what an energy baseline is, it is a detailed summary of your energy cost and consumption data for at least twelve months. By creating an energy baseline you’re establishing an important benchmark for your organization to measure future energy and environmental initiatives to.

As important as it is to have data for at least twelve months, it is also important for this data to be for consecutive months. Although there are several mechanisms to estimate future energy consumption, starting from a baseline period, having only a partial data set will have an impact on the accuracy of future projections and data analysis.

Every month of energy data, for a specific baseline period, brings with it an array of factors that influenced its results, such as:

  • Weather (i.e. requiring higher/lower cooling and or heating);
  • Specific operational behaviour (i.e. peak distribution months for a company requiring their warehouse doors to remain open more hours every day); and
  • Higher external lighting consumption during winter months vs. summer months.

Therefore, although missing energy data for specific months within a baseline period can be estimated, the accuracy of estimations would be lower as these specific factors would be missing.

The following graph shows a clear example of the relationship between completeness of energy data and the accuracy of its analysis and projections.

Figure 1 – Quality of Data Set & Accuracy of Analysis

energy reporting

The closer a baseline period gets to being fully complete (i.e. 90% to 95% or 11 out 12 months) the more accurate the analysis and projections will be.

Two main components of energy analysis are cost and consumption. Most organizations keep track of their energy cost only (through their Finance and Accounting department) leaving out the other half of the equation -Consumption. Utility invoices are not only the result of rate and consumption; there are several other charges and adjustments included in it, which should also be taken in consideration. This only clarifies the need for both sides of the equation.

In summary, when it comes down to energy management, the size and completeness of your energy data is a key factor for data transparency, accurate analysis and a benchmark for future energy initiatives.

Hugo is responsible for managing the Data Management & Reporting department at Energy Advantage Inc.

2 thoughts on “Historical Energy Data and Accuracy of Analysis and Projections

  • Very interesting Hugo. Can you comment as to how increased accuracy allows organizations to better capture billing errors?

  • Pat: Its not uncommon for utility bills to have many errors, such as, incorrect read dates, poor estimates, incorrect energy usage and even total billed amount. By examing your utility data through a detailed quality assurance process will allow you to identify these errors and validate the data. Today, companies need to ensure there data is accurate and transparent, especially with stronger environmental reporting disclosure requirements. It’s also important to have accurate data when you are performing any type of analysis. The more accurate the data, the better the analysis will be.

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