• Overview
• Visitors
• People
• Links

• Groups
• Publications
• Software

• Courses
• SA/MA
• Lab

• News
• Seminars
• Jobs

Building State Estimation

Overview

An increase in energy use efficiency is a key to reducing our dependency on fossil fuels and limiting of worldwide CO2 emissions. Approximately 40% of the global used energy is consumed in buildings, of which roughly half is used for Heating, Ventilation and Air Conditioning (HVAC). At the same time, most investments in building energy efficiency can be expected to pay back through reduced energy bills.

Improved HVAC control is particularly interesting because of the relatively low cost of improved control solutions as compared to other refurbishments in buildings, the comparatively short (10-15 years) lifetime of HVAC systems, and, last but not least, thanks to advances in sensor networks and information technologies that open up entirely new opportunities for advanced building control. Challenges relate to the uniqueness of many buildings, the possible complexity of their technical systems, and the building industry’s fragmentation.

A large scale simulation study carried out with participation of IfA showed model predictive control (MPC) incorporating weather and occupancy forecasts to have the potential of significant energy savings in many building set-ups. This year we started together with industrial partners (Siemens, Gruner) a follow-up project to implement an MPC controller in a representative office building of Actelion Pharmaceuticals Ltd in Basel. The thesis work can be expected to contribute to this project.

Project Description

Aside of modeling the system dynamics, another key to successful application of MPC in a building is the correct estimation of the system’s current state. We have equipped a whole floor of the Actelion building with wireless sensors (room temperatures, presence sensor, window contacts, humidity, CO2) and have access to all the measurements related to the HVAC (supply/return water temperatures, ventilation air mass flow/temperature, etc.).

The goal of this thesis is to find a way of combining these data together with a model of the building to optimally estimate the state of the building (which includes not directly measurable temperatures e.g. concrete core temperatures). Another point of interest relates to the minimum number of sensors needed to enable a sufficiently good state estimation . This work will make use of the measurement data that are currently being collected in the building. A well working state estimation algorithm will be employed in the Actelion building.