How can simulation techniques identify energy waste in a building?

Simulation techniques offer a virtual sandbox for energy managers to experiment with different scenarios. They enable modeling complex building systems, providing insights into energy performance without real-world consequences.

The root cause of the waste can be the vital and basic one to groom your system, integrated systems can help you better to find out the waste ratios and the loop holes to carry out operation in smooth way, in my perspective IOT is best in place to get to know the waste energy very well.

Overall, simulation techniques offer a powerful means of identifying energy waste by providing detailed insights into energy consumption patterns, conducting sensitivity analyses, optimizing operating parameters, facilitating virtual audits, performing scenario analysis, and enabling real-time monitoring and control. By leveraging simulation tools effectively, organizations can identify opportunities to reduce energy waste and improve overall energy efficiency.

Simulation of a building's energy behavior and comparing this to industry-wide benchmarked consumption profiles is crucial to identify energy waste in large building portfolios. Experts can do this on a small scale in a labor-intensive way manually, but this is not scalable. There are generally 2 main approaches: - Data-driven / AI based methods - Rule based / white box models Once again, rule based methods lack accuracy and specificity: every building is different and changes over time. An AI-based digital twin that is fully self-learning is the only method that combines accuracy, scalability and affordability due to the total lack of configuration needed for 24/7 waste surveillance

Doing this on a very regular basis is important to pinpoint the waste…ie. Half hourly intervals. Otherwise it will be difficult to identify the waste.

Simulation techniques precisely pinpoint energy waste by comparing expected energy usage with actual consumption. They unveil inefficiencies stemming from equipment, controls, insulation, or human behavior, guiding targeted interventions for energy savings.

Simulation techniques enhance energy management by refining audits, optimizing system design, and enabling real-time monitoring. They empower decision-making through detailed data analysis and evidence-based recommendations, fostering energy efficiency.

Despite their benefits, simulation techniques demand expertise and substantial data inputs. Their complexity requires skilled calibration and validation, and interpretation of results involves time and resources. Uncertainty from assumptions underscores the need for careful analysis.

Improving simulation techniques involves integrating tools, customizing models, and streamlining processes. This enhances accuracy, reduces complexity, and ensures adaptability to evolving building dynamics, fostering more effective energy management.

The elephant in the room is often: how do I get my energy data linked to these advanced simulation techniques and AI at scale, without ending up with huge IT-costs or terrible data-quality. This is where the right energy data-platform selection is crucial to enable energy diagnostics afterwards. A lot of energy monitoring systems offer basic alarm modules or reporting. However, they almost always lack serious analytics functionality, let alone AI once you dive deeper. To avoid complete lock-up in a legacy system, always choose an open IoT or EMS platform with a free or very affordable API that is well documented. The evolution of the energy market will require multi-connectivity. Do not trust any vendor that says "we can do everything"