How can you use software to prioritize energy efficiency projects?

Prioritize quick wins with lasting impact: Focus on readily achievable actions with significant cost savings to build momentum and garner support. Remember, it's not just about dollars - consider additional benefits like improved comfort, health, and productivity that enhance the business case. Finally, get everyone on board: Present clear data and showcase the tangible benefits of prioritized projects to secure buy-in from key stakeholders.

Utilize IoT sensors to capture granular real-time data on equipment health, occupancy, and environmental conditions. This data can uncover hidden inefficiencies and inform targeted interventions.

Leverage digital twins! Create virtual replicas of your facilities to test various efficiency measures in a safe, simulated environment. This helps you identify the most impactful upgrades before investing real resources.

If you collect retro-commissioning data on your facilities and track in one centralized location, you can easily search what energy measures are the most urgent, which will result in the most energy savings, or by cost across the portfolio of properties. This will allow the team to prioritize projects based on the needs, budget, and goals of the organization. You can also track post project performance to determine trends and success based on the projects that have been implemented.

Running analysis on the available data in an automated fashion is essential to make this step make sense at scale. 1 energy engineer with Excel and a large data set is woefully inefficient at this task, in comparison to the ability to analyze data with dedicated software that is purpose developed and trained/programmed to identify optimization opportunities and defects in the buildings which are leading to the inefficiencies.

Data integrity is first and foremost. This is achieved by verifiying that measured data is accurate through point to point testing and Commissioning. Next proper care should be used to ensure trend type, stored sample count and sampling frequency are set and appropriate for systems being measured. Lastly selecting a methodology of reliably exporting trends to a cloud based database or analytic software suite should be evaluated. (Message me if you have specific needs) Once the data is stored in a secure location it can then be manipulated , AI ECM detection applied, and displayed in a dashboard, PowerBI graphic, or essentially utilized in any fashion one can dream up (and skillset can support)

Empower AI and machine learning! Train algorithms on your historical data to predict energy consumption patterns and automatically recommend optimal efficiency measures. This saves time and ensures data-driven decisions.

There are many commercial softwares that can be used to evaluate energy efficient measures. From lighting to HVAC and efficient plug loads.

Simulation and energy models have been around for some time but do not always reflect actual operating conditions and variables such as building tightness or proper sequencing to take advantage of equipment part load efficiencies. Developing an advanced sub load metering strategy in conjunction with laying out a facilities electrical distribution system allows you to obtain realtime energy data. This allows you to compare actual performance to the initial energy model. In my 20 years of hands on experience buildings rarely perform to modeled predictions. Sequencing tweaks, scheduling and PID tuning can then be tested and effects realized in the form of real-life energy reduction in lieu of relying on predictions imperfect models.

Unlock the power of reinforcement learning! Let AI agents interact with your digital twin, iteratively learning and refining energy-saving strategies based on real-time feedback. This optimizes performance beyond simple simulations.

Hi! If anyone is looking for a tool for their resi and smaller C&I projects, my company is developing software for this specific issue. We also love to do exploratory calls to learn more about the specific pain points. We built and led 3 direct-to-consumer solar companies in the Americas for 5 years and were always really frustrated about the fragmented and Excel-heavy solutions we had to use to plan installations and then monitor alerts & and performance, so we built a tool based on our experience. It lets you monitor data from your assets, your team, and your customers all in one place!

Of course this is the most accurate way of evaluating savings and energy use. Monitoring can be done by checking consumption over demand profile. However sometimes consumption baseline can be change due to end user behaviour and this is to be considered when monitoring or evaluating energy savings

Connectivity is by far the very best way to monitor energy consumptions and to be sustainable. Digitalization of energy consumption and real time trends that are visible for monitoring will be a key to obtain sustainable energy management.

Embrace the power of IoT dashboards! Track project progress, equipment performance, and energy savings in real-time through intuitive dashboards. This fosters transparency, accountability, and swift course correction.

Generate data-driven insights! Utilize machine learning to analyze vast datasets and uncover hidden correlations between implemented measures and achieved savings. This provides valuable insights for future optimization.

The use of Big data analytics helped a lot in understanding the correlation with various process parameters hitherto not possible to perform with traditional software. The AI enabled big data analytics is the future tool to assess, evaluate and prioritize Energy efficiency programs. In our plant, we have been able to do this and identify additional potential energy saving area apart from the traditional findings based on energy audit.

To prioritize energy efficiency projects using software, a systematic approach can involve evaluating factors such as potential energy savings, payback periods, and environmental impact. AI can enhance this process by leveraging machine learning algorithms to analyze vast datasets, identify patterns, and predict project outcomes, facilitating data-driven decision-making for optimal resource allocation and project prioritization in energy efficiency.