Why Energy Data is Key to Your Decarbonisation Journey

energy data engineer worker team discussing of solar energy from wind turbin
ankit shringi anthesis

Ankit Shringi

Senior Consultant

Australia

GHG emissions due to human activity are the main cause that has led to climate change. A “Climate Spiral” the famous visualisation created by climate scientist Ed Hawkins which effectively demonstrates gradually increasing global average temperatures which have risen by +1°C since circa 1980 (Hawkins, 2022). To stall this phenomenon of global warming, we must now act immediately to reduce greenhouse gas (GHG) emissions drastically and limit the global temperature rise to +1.5°C, beyond which we will start to see irreversible changes to the environment.

Accordingly, we must shift to a low-carbon economy that is based on energy sources producing much lower levels of GHG emissions.

This “Decarbonization” of our economies is not easily achievable solely by switching to less emission intensive sources. Along with switching to low emission-intensive energy sources, we must also reduce energy consumption by making our industries more energy efficient and evolving operational policies to suit this transition.

Efficient energy data monitoring plays a key role in this transition.

Benchmarking energy data and productivity

The first step an organisation can take in its decarbonisation journey is to understand and benchmark its energy data and productivity – the latter of which has been defined by the Australian Alliance for Energy Productivity (A2EP) as “an indicator of the amount of economic output that is derived from each unit of energy consumed”

Energy productivity is an indicator of the amount of economic output that is derived from each unit of energy consumed.A2EP

Improving energy productivity has at least two-fold benefits for an organisation – a reduction in expenses towards the cost of energy and more importantly, a reduction in the amount of carbon emissions because of reduced energy consumption in the form of imported electricity, gas and other fuels.

However, it is important to understand that the most important aspect is the capture of correct energy data.

Capturing the correct energy data is key

Captured energy data enables a facility to paint a detailed picture of their energy consumption including peak energy consumption durations, identify the most energy-hungry equipment, as well as operational aspects that contribute to the overall pattern of energy consumption.

Robust data can serve as the foundation for establishing a benchmark for energy performance which can then be improved by the deployment of better technology (energy efficient), reducing energy waste by recapturing lost energy (usually as heat), improving operational processes or any combination of the above.

In order to establish a baseline for energy performance of a facility, we need to capture relevant and granular energy data for discovering areas that can benefit from intervention. It is important to capture details surrounding the operational process along with efficiencies of all equipment that use energy. These details can reveal any potential energy recovery and re-utilisation opportunities that may exist.

In summary, it is important to capture data from every facet of the process that can potentially contribute to energy consumption in the facility.

Insights – building your energy data model

The captured data when transformed, forms the basis for analysis of current conditions, decision-making regarding applicable improvements and for tracking the progress of implementation projects. It is critically important to consider the right metrics, functional units and dependent as well as independent variables for creation of a robust model, representing operations at the facility. Such a data model can then be used for setting goals and defining targets towards improving energy performance and contributing towards decarbonisation.

Analysis of data captured from energy audits can reveal insights that can be used to set targets towards achieving better energy productivity and contribute to overall decarbonisation. It is important that we set targets that can be achieved by sustainable performance. For this purpose, we suggest use of the SMART framework.

SMART is an abbreviation for Specific, Measurable, Achievable, Relevant and Time-bound. SMART goals are important for achieving sustainable performance towards goals as they allow key decision-makers to divide a goal into smaller bite-sized targets, that can be assigned to focused teams and individuals. SMART goals can then lead to projects aimed at improvements in specific areas of the facility.

energy data and modelling decarbonisation ndevr environmental
EXAMPLE OF DATA MODELLING: ©NDEVR ENVIRONMENTAL

Data variables and continuous improvement

It should, however, be noted that data models customised to capture the important variables pertaining to the process involved, underpin the ultimate performance of improvement projects.

For example, an electricity generating facility must understand all the “variables” such as fuel being used for electricity generation, technology employed, auxiliary loads, and climactic factors and establish a relationship between them (data model), before deciding on a project aimed at improving energy productivity as well as reducing total emissions for each unit of electricity produced. SMART goals combined with data models can then allow generation of visualisations, such as interactive dashboards, for real-time performance monitoring and defining long-term goals.

Most organisations employ a Plan-Do-Check-Act (PDCA) cycle for continuous improvement and for keeping on track with their long-term goals. Periodic data capture and performance-monitoring against an established energy performance model allows key decision-makers to engage in a “course-correction” activity when performance outputs suggest a gap between forecast and actual energy performance. Any miss-outs (better terminology required) in energy performance also suggest higher than expected carbon emissions, since more energy than expected was used.

Robust data models combined with periodic energy data capture, is thus key to successful outcome towards your decarbonisation journey.

Need guidance or advice to understand how you can improve your energy data or performance?

With rapidly escalating costs and uncertainty ahead in the market, energy is at the forefront of many a business leader’s mind. Businesses should be putting a strategy in place now to benchmark energy data across the organisation, ensure the capture of correct data, build an energy data model to transition towards a cost-efficient, decarbonised business.

We have a team of energy experts who provide a range of specialised services for businesses to manage and reduce energy consumption and promote energy efficiency and also net zero strategies and plans. Reach out to learn more.

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