systems (Palmer et al. 2016). However even if the system has the capacity to store data, in practise it was found that recorded data are vulnerable to the system being reset or altered.
3.2
Dataset A: School
It has been previously noted that building managers and designers are often not com- fortable sharing information about their buildings which could highlight poor design or operation. The first dataset used in this project is from a school in the North of England. The data were collected by the company responsible for managing and fine-tuning the BMS. Unfortunately, in this case some of the stakeholders were uncomfortable with build- ing data being used. While permission was given for the building data to be used, there were certain conditions attached. The primary stipulation was a degree of anonymity. Therefore the building is described but the author is precluded from naming it specifi- cally. It was also not possible to arrange a visit to the building and make any first hand observations. While these constraints were unfortunate, the volume and quality of data available made its study worthwhile.
The inclusion of data from a school within this project was also seen as beneficial. The quality of the internal environment and its effect upon the occupants ability to learn is a active area at the moment, both within the research community and legislation. There are a number of studies which find a relationship between the ventilation rate and pupils performance (Bak´o-Bir´o et al. 2012, Daisey et al. 2003, Shaughnessy et al. 2006). Also natural ventilation has been used a lot in schools built as part of the ‘Building Schools for the Future’ programme (Santamouris et al. 2007). Schools are likely to be an ideal target for the application of the control methodology proposed in this project.
3.2.1 Description of Spaces
The school was built within the past five years as part of the Building Schools for the Future programme. It is an all-through school, i.e. provides teaching for students aged 3-16. The building is of lightweight construction, with a layout based around a central atrium with four wings radiating outwards.
The school is predominately naturally ventilated, with manual occupant controlled win- dows at low level and automated windows linked to the BMS at high level. For this study, eight classroom spaces were selected. They were chosen in an effort to select a range of spaces with different orientations and ventilation scenarios (summarised in Table 3.1). Nothing is known about how the occupants use the space beyond the room description provided on the building plans. In this case obtaining detailed information about the occupancy patterns was not possible due to constraints regarding access to the building. Even without these constraints, compiling detailed information related to occupancy pat- terns for a building of this size would be very time consuming, as such it is not likely to
Chapter 3. Data Collection and Pre-Processing 41
Zone Floor Usage Orientation Ventilation Scenario
1 Ground Humanities classroom North Windows on three sides
2 Ground Humanities classroom North-west Single-sided
3 Ground Humanities classroom East Single-sided
4 Ground Humanities classroom East Single-sided
5 First Junior classroom South Single-sided
6 First Junior classroom North-west Windows on north and west
7 First English classroom South-west Windows on north and west
8 First English classroom North-west Windows on south and west
Table 3.1: Description of spaces in dataset A.
be a method used in a commercial application of MPC. Information of this kind would be necessary to develop accurate physics based models of the space using dynamic thermal simulation. The lack of available occupancy information when applying MPC to buildings is a key justification to utilise a black-box modelling approach.
3.2.2 Recorded Variables
Environmental conditions and window state data were collected over a period of eighteen months using sensors linked to the BMS. The frequency of recorded observations varied over the period. The frequency varied as some observations are stored based upon a control action being taken, in addition to the regular recording of one observation every five minutes. The environmental variables recorded were temperature, carbon dioxide concentration and relative humidity. In each zone there is only one value for each variable, i.e. classrooms have not been subdivided into smaller zones. As mentioned in the preceding section, windows were a mixture of occupant controlled manual windows and automated windows. The opening state of the automated windows was recorded by the BMS based upon the window actuator position, given as an opening percentage between 0 and 100% at intervals every 10%. The condition of the space heating was also recorded, this was stored as a boolean (on/off) value.
No information was available for the manual windows. This was because the manual windows are not equipped with sensors. If access to the building had been possible, data for the window opening state of the manual windows could have been gathered using data loggers. This would have given a boolean (open/closed) variable for the manual windows. However, one of the main motivations of this project is to develop a control strategy which can easily be applied to a range of buildings. As of yet the author has not encountered a building where the opening position of manual windows are logged by the BMS. Ideally, models should be capable of capturing the effect of the automated windows, while treating manual windows as an unmeasured disturbance. If this is possible it would allow for models to be developed for existing building stock without having to install further monitoring equipment or additional sensors.