Technical Clarifications

By Chapter

The following notes provide technical clarification for selected parts of The Story Behind BirdLab podcast conversation.

10. Web analytics

The reference to the World Wide Web “going public” in 1993 refers specifically to CERN's decision on 30 April 1993 to release the Web software into the public domain. The Web was already in operation before this, including on servers outside CERN.

16. Networked controllers

The 0–10 V sensor and actuator examples are illustrative rather than universal. Temperature inputs may, for example, be resistive thermistors or RTDs as well as transmitters producing standard analogue signals such as 0–10 V. Actuator signal types and their relationship to physical valve position also vary.

24. Building Energy Management Systems (BEMS)

The discussion of heating and cooling energy being proportional to airflow assumes other relevant variables are held constant. For sensible heating or cooling, thermal power is proportional to mass airflow when specific heat capacity and temperature difference are fixed. The statement refers to the energy required to condition that air, rather than total building energy consumption.

29. Savings calculation methodology

The discussion refers to the ASHRAE Guideline 14 calibration criterion commonly expressed as CV(RMSE). The 15% figure refers to the monthly CV(RMSE) criterion, rather than unnormalised root mean squared error. Model calibration may also be assessed using other metrics, including NMBE.

During the discussion, the host refers to approximately 4% average model error as “96% accuracy”. This is conversational shorthand rather than a formal statistical definition of accuracy. The figure being reported is approximately 4% average error when backtesting the factual models against actual metered energy consumption.

33. 6th startup: special purpose vehicle

“Informatics” here refers to the application of data structuring, system interrogation, and semantic interpretation techniques to extract actionable knowledge from building automation data. It enables automated reasoning with regard to HVAC systems configuration and performance, forming the foundation for scalable optimisation.