ShinyRBase: Near real-time energy saving models using reactive programming

To document energy savings from retrofitting a building, a reliable baseline model is needed. The development and implementation of the baseline model is an important step in the measurement and verification (M&V) process. Usually, an energy analyst enters the stage, collects data, do the estimation and delivers the baseline model. The modeling work of the energy analyst is done on either a proprietary or open-source statistical software, often using a coding script. If stakeholders want an updated report on energy savings, the analyst must re-do the whole process, for example on a monthly basis. This workflow is based on an imperative programming paradigm. The analyst holds on to the code that performs the analysis and re-run the code when agreed upon. The consequence of this workflow is that stakeholders are dependent on the energy analyst and that updated energy savings results must be planned and scheduled. However, emerging M&V 2.0 technologies enables automation of the energy saving reports. This paper demonstrates how energy savings from retrofitting’s in the Norwegian food retail sector is continuously monitored and documented in a web application. The application is built using open-source tools where the baseline model is delivered through a reactive programming framework. As an energy savings baseline model, the Tao Vanilla benchmarking model (TVB) was set into production in the web application. The TVB is a linear regression model with well specified features, easy to interpret and has a history of excellent prediction performance. The proposed web application framework allows for a fast development cycle without any need-to-know web programming languages like HTML, CSS or JavaScript. The reactive framework delivers several advantages. First, the stakeholders will always have a current and real-time report on the savings. Second, complex methodologies are dynamically used by the end-user. Third, increased involvement by stakeholders and interaction with the analyst related to the methods used in the energy savings analysis leads to collaborative benefits such as faster disseminating of knowledge. These synergy effect leads to a better technical understanding from the end user perspective and enhanced practical understanding for the analyst. Finally, the paper presents an integrated look at the energy kWh savings versus the cost of the retrofitting’s.