Designing sensor sets for capturing energy events in buildings

There is a growing desire to measure the operational performance of buildings – often many buildings simultaneously – but the cost of sensors and complexity of deployment is a significant constraint. In this paper, we present an approach to minimising the cost of sensing by recognising that researchers are often not interested in the raw data itself but rather some inferred performance metric (e.g. high CO2 levels may indicate poor ventilation). We cast the problem as one of constrained optimisation – specifically, as a bounded knapsack problem (BKP) – to choose the best sensors for the set given each sensor's predictive value and cost. Training data is obtained from a field study comprising a wide range of possible sensors from which a minimum set can be extracted. We validate the method using reliable self-reported event diaries as a measure of actual performance. Results show that the method produces sensors sets that are good predictors of performance and the optimal sets vary substantially with the constraint parameters. Furthermore, valuable yet expensive sensors are often not chosen in the optimal set due to strong co-incidence of sensor signals. For example, light level and sound level often increase at the same time. The overall implication of the work is that a large number of co-incident low-cost sensors can be used to build up a picture of building performance, without significantly compromising information content, and this could have major benefits for the smart metering industry

Designing sensor sets for capturing energy events in buildings

Belgium Herbarium image of Meise Botanic Garden

Designing sensor sets for capturing energy events in buildings

There is a growing desire to measure the operational performance of buildings – often many buildings simultaneously – but the cost of sensors and complexity of deployment is a significant constraint. In this paper, we present an approach to minimising the cost of sensing by recognising that researchers are often not interested in the raw data itself but rather some inferred performance metric (e.g. high CO2 levels may indicate poor ventilation). We cast the problem as one of constrained optimisation – specifically, as a bounded knapsack problem (BKP) – to choose the best sensors for the set given each sensor's predictive value and cost. Training data is obtained from a field study comprising a wide range of possible sensors from which a minimum set can be extracted. We validate the method using reliable self-reported event diaries as a measure of actual performance. Results show that the method produces sensors sets that are good predictors of performance and the optimal sets vary substantially with the constraint parameters. Furthermore, valuable yet expensive sensors are often not chosen in the optimal set due to strong co-incidence of sensor signals. For example, light level and sound level often increase at the same time. The overall implication of the work is that a large number of co-incident low-cost sensors can be used to build up a picture of building performance, without significantly compromising information content, and this could have major benefits for the smart metering industry

Designing sensor sets for capturing energy events in buildings

We study the problem of designing sensor sets for capturing energy events in buildings. In addition to direct energy sens- ing methods, e.g. electricity and gas, it is often desirable to monitor energy use and occupant activity through other sensors such as temperature and motion. However, practical constraints such as cost and deployment requirements can limit the choice, quantity and quality of sensors that can be distributed within each building, especially for large-scale deployments. In this paper, we present an approach to select a set of sensors for capturing energy events, using a measure of each candidate sensor’s ability to predict energy events within a building. We use constrained optimisation – specif- ically, a bounded knapsack problem (BKP) – to choose the best sensors for the set given each sensor’s predictive value and specified cost constraints. Our approach arises from a field study of 4 UK homes with temperature, light, motion, humidity, sound and CO2 sensors. By using random forests to generate a measure of each sensor’s predictive value, and financial cost as a measure of each sensor’s cost, the results show that these environmental sensors are useful predictors of energy use, though the optimal sets vary substantially with the constraint parameters. Furthermore, valuable yet expen- sive sensors such as CO2 are often not chosen in the opti- mal set, and a proportion of both CO2 and light level can be predicted from the other environmental sensors used in the study

ENLITEN - A dataset and its associated analysis code for the paper entitled "Designing sensor sets for capturing energy events in buildings"

This dataset contains data and software source code supporting the paper entitled 'Design sensor sets for capturing energy events in buildings'. It contains raw data from sets of domestic sensors measuring temperature, humidity, levels of sound, light and carbon dioxide, and power consumption. It also contains analysis code and visualisation code written for R and Python.Details of the methodology may be found in the associated manuscript. All data was collected approximately from Aug 2013 to Oct 2013. An instruction for the participants attached here (instruction.pdf) shows the way how the data was collected in the individual homes.The data files are in comma-separated (.csv) and tab-separated (.txt) plain text formats. The .py source code files target Python v2.x and use only standard libraries. The R source code files use the following libraries available from CRAN: entropy, ggplot2, plyr, randomForest, reshape2, scales. Some pre-compiled visualisations are included as PDF files.The zip file contains a set of top level data and source code files, a further files arranged into 5 directories. The 'analysis_datasets' directory contains the main dataset used for the paper. It also includes the machine learning algorithm code (random forest in this case), its associated data and a document showing a selected feature set. The 'gt', 'sd', and 'tf' directories contain raw data collected by one of participants. The 'set_viz' directory contains visualisation code as well as knapsack (cost–benefit) optimisation code for the selection of minimal sensor sets subject to the budget