Energy consumption prediction from usage data for decision support on investments: the EnPROVE approach

Abstract

Abstract When intending to renovate an existing building, with energy efficiency and greenhouse gas emissions in mind, a building owner is always questioning himself if the available investment resources are being directed to an effective return and if there are ways to improve this return? This paper presents the innovative approach from EnPROVE project that responds the previous question in a positive way. The approach is based on predicting the energy consumption of a specific building, with different scenarios implementing energy-efficient technologies and control solutions, based on actual measured performance and usage data of the building itself. The key hypothesis of EnPROVE is that it is possible, from adequate gathering and assessing data on how a structure performs and is being used by its occupants from an energy viewpoint, to build highly accurate and specific energy consumption models relevant for prediction of alternative scenarios. The EnPROVE software tools assess the energy-efficiency impact of alternative technologies for which available investment resources can be directed and, thus, support the decision maker finding the optimized set of energy-efficient solutions to be implemented. These results are tailored to the actual building itself, through automated measurements of building usage and energy consumption.