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

Implementing an integrated meter and sensor system (IMSS) in existing social housing stock

The current rollout of smart meters for gas and electricity, both in the UK and internationally, will help suppliers to better forecast demand and supply accurate bills to consumers. However, even with an in-home display (IHD), the benefits of a smart meter to a domestic customer are limited by the so-called ‘double invisibility’ of energy [1] and the standardisation of IHD design for an imagined home ‘micro-resource manager’ [2]. Furthermore, low-income households may be limited in the benefits they can reap from such systems; already living within a tight budget, suggestions for further energy-related cost savings may be detrimental to their health and wellbeing. This makes it important that the impact of actions taken to save energy is communicated. This can be done using indoor environmental measures, including carbon dioxide, relative humidity and temperature, as part of an integrated meter and sensor system (IMSS) and an associated IHD or digital application. Such a system gives users the ability to make informed decisions about their energy use and indoor environmental health. This paper explores the potential barriers to implementing an IMSS in practice. It explains how an IMSS was designed, based on a review of meter and sensor systems; details the process is taken to trial the IMSS in 19 social housing properties in the English Midlands; and makes recommendations for a larger scale rollout of IMSSs. The paper also reviews current progress in cloud storage and security as relevant to IMSSs and smart metering

Moving to a green building: Indoor environment quality, thermal comfort and health

A global movement towards the creation of “green” buildings is currently underway. Although driven primarily by an external environmental agenda such as energy or carbon, there is growing recognition that greener buildings could affect the Indoor Environment Quality (IEQ). However, localised green building codes, especially in the developing world, often do not systematically recognise IEQ or health as crucial issues, which therefore remain understudied. Since the developing world alone is expected to nearly double current global built floor space by 2050, it is crucial that green buildings perform holistically to be effective. Here, we follow 120 employees of a single organisation as they transition from four conventional office buildings to the first green building (GB), designed to the local Jordanian Green Building Guide. We ask if the move has a positive effect on occupant perception of IEQ, thermal comfort and prevalence of Sick Building Syndrome (SBS), using a repeated-measures protocol. Statistically significant differences in thermal conditions, positively biased towards the GB, were observed across the move, and this enhanced occupant thermal comfort. Surprisingly, no significant improvement in occupant perception of air quality, visual and acoustic comfort was detected after moving to the GB, while odour, mental concentration, and glare were perceived to be poor in the GB and associated with an increase in the prevalence of SBS symptoms. Hence, our results support the growing concern that green buildings may create unintended consequences in terms of occupant comfort and health in the pursuit of a better thermal environment and energy efficiency

Moving to a green building: Indoor environment quality, thermal comfort and health

A global movement towards the creation of “green” buildings is currently underway. Although driven primarily by an external environmental agenda such as energy or carbon, there is growing recognition that greener buildings could affect the Indoor Environment Quality (IEQ). However, localised green building codes, especially in the developing world, often do not systematically recognise IEQ or health as crucial issues, which therefore remain understudied. Since the developing world alone is expected to nearly double current global built floor space by 2050, it is crucial that green buildings perform holistically to be effective. Here, we follow 120 employees of a single organisation as they transition from four conventional office buildings to the first green building (GB), designed to the local Jordanian Green Building Guide. We ask if the move has a positive effect on occupant perception of IEQ, thermal comfort and prevalence of Sick Building Syndrome (SBS), using a repeated-measures protocol. Statistically significant differences in thermal conditions, positively biased towards the GB, were observed across the move, and this enhanced occupant thermal comfort. Surprisingly, no significant improvement in occupant perception of air quality, visual and acoustic comfort was detected after moving to the GB, while odour, mental concentration, and glare were perceived to be poor in the GB and associated with an increase in the prevalence of SBS symptoms. Hence, our results support the growing concern that green buildings may create unintended consequences in terms of occupant comfort and health in the pursuit of a better thermal environment and energy efficiency

Development of an open-architecture temperature data logger for hydro-distillation agarwood oil extractor

Microcontroller based data logger system recently emerged as a powerful, flexible and cost-effective measurement solution to many innovative field applications in environmental monitoring, agriculture and solar energy. Real-time temperature is vital to provide discrete knowledge in the process of distillation as it involves mixture boiling, evaporation and condensation at the difference in liquid phases. The development of a data logger namely OCTATherm for use in the Agarwood extraction industry is realized by designing an electronic enclosure to protect an Arduino-based microcontroller system that can acquire eight (8) thermocouples readings for monitoring the hydro-distillation process. The accuracy and reliability of the data logger have been evaluated by assessing the hydro-distillation (HD) process on a laboratory scale by comparing its performance to the commercial data logger (HOBO UX120). Finally, the assessment in the industry with multi-boiler operate simultaneously shows that real-time monitoring of the temperature measurements at critical points of the conventional HD system can improve the yield of the extracted Agarwood essential oil by three (3) times higher from 0.027% to 0.101%. The implementation of real-time thermal management technology in the HD system in the Agarwood essential oil production industry is therefore of great importance. This developed data logger is significant to produce a real-time data acquisition and monitoring platform of temperature measurement, which aims to facilitate agriculture industry process monitoring as well as academic research purpose in another area. The open-architecture based system design is also highlighted in which provides future upgrades of expansion and extension features of the data logger