A review of human thermal comfort in the built environment

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for text and a handful of other datatypes. However, surprisingly little progress has been made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for two time series data mining problems, including motif discovery and novelty discovery.

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

Published research on smart homes and their users is growing exponentially, yet a clear understanding of who these users are and how they might use smart home technologies is missing from a field being overwhelmingly pushed by technology developers. Through a systematic analysis of peer-reviewed literature on smart homes and their users, this paper takes stock of the dominant research themes and the linkages and disconnects between them. Key findings within each of nine themes are analysed, grouped into three: (1) views of the smart home--functional, instrumental, socio-technical; (2) users and the use of the smart home--prospective users, interactions and decisions, using technologies in the home; and (3) challenges for realising the smart home--hardware and software, design, domestication. These themes are integrated into an organising framework for future research that identifies the presence or absence of cross-cutting relationships between different understandings of smart homes and their users. The usefulness of the organising framework is illustrated in relation to two major concerns--privacy and control--that have been narrowly interpreted to date, precluding deeper insights and potential solutions. Future research on smart homes and their users can benefit by exploring and developing cross-cutting relationships between the research themes identified.

Smart homes and their users: a systematic analysis and key challenges

Study objective: This study investigated the relation between heat and mortality in London to determine the temperature threshold at which death rates increase and to quantify the effect of extreme temperatures on mortality. Design: Daily data on all cause mortality and temperature were obtained for a 21 year period and the relation between them investigated both graphically and by using non-parametric time series methods of analysis. Setting: Greater London. Participants: Daily mortality counts in Greater London between January 1976 and December 1996. Main results: A plot of the basic mortality-temperature relation suggested that a rise in heat related deaths began at about 19°C. Average temperatures above the 97th centile value of 21.5°C (excluding those days from a 15 day “heatwave” period in 1976) resulted in an increase in deaths of 3.34% (95% CI 2.47% to 4.23%) for every one degree increase in average temperature above this value. It was found that the 1976 heatwave resulted in a particularly large number of deaths in comparison with other hot periods. Conclusions: These results suggest that heat related deaths in London may begin at relatively low temperatures. Hot days occurring in the early part of any year may have a larger effect than those occurring later on; and analysis of separate heatwave periods suggest that episodes of long duration and of highest temperature have the largest mortality effect.

The impact of hot temperatures on death in London - A time series approach

Benefits and risks of smart home technologies

Internal summertime temperatures measured in 268 homes in Leicester, UK, are reported. The hourly data were collected from living rooms and bedrooms during the summer of 2009. Some household interviews were conducted. The sample of homes was statistically representative of the socio-technical characteristics of the city's housing stock. The data provide insight into the influence of house construction, energy system usage, and occupant characteristics on the incidence of elevated temperatures and thermal discomfort. The warmest homes were amongst the 13% that were heated. Significantly more of these had occupants aged over 70 years who are particularly vulnerable to high temperatures. The national heatwave plan might usefully caution against summertime heating. Temperatures in the 230 free-running homes were analysed using both static criteria and criteria associated with the BSEN15251 adaptive thermal comfort model. These indicated that flats tended to be significantly warmer than other house types. Soli...

/pdf/summertime-temperatures-and-thermal-comfort-in-uk-homes-22t9yhhpn4.pdf

Summertime temperatures and thermal comfort in UK homes

How are UK homes heated? A city-wide, socio-technical survey and implications for energy modelling

This paper presents a data collection and energy fe edback platform for smart homes to enhance the value of information given by smart energy meter da ta by providing user-tailored real-time energy consumption feedback and advice that can be easily accessed and acted upon by the household. Our data management platform consists of an SQL server back-end which collects data, namely, aggregate power consumption as well as consumption of major appliances, temperature, humidity, light, and motion data. These data streams allow us to infer information about the household’s appliance usage and domestic activities, which in t urn enables meaningful and useful energy feedback. The platform developed has been rolled ou t in 20 UK households over a period of just over 21 months. As well as the data streams mentioned, q ualitative data such as appliance survey, tariff, house construction type and occupancy information a re also included. The paper presents a review of publically available smart home datasets and a desc ription of our own smart home set up and monitoring platform. We then provide examples of th e types of feedback that can be generated, looking at the suitability of electricity tariffs a nd appliance specific feedback.

A data management platform for personalised real-time energy feedback

By introducing new ways of automatically and remotely controlling domestic environments smart technologies have the potential to significantly improve domestic energy management. It is argued that they will simplify users lives by allowing them to delegate aspects of decision-making and control - relating to energy management, security, leisure and entertainment etc. - to automated smart home systems. Whilst such technologically-optimistic visions are seductive to many, less research attention has so far been paid to how users interact with and make use of the advanced control functionality that smart homes provide within already complex everyday lives. What literature there is on domestic technology use and control, shows that control is a complex and contested concept. Far from merely controlling appliances, householders are also concerned about a wide range of broader understandings of control relating, for example, to control over security, independence, hectic schedules and even over other household members such as through parenting or care relationships. This paper draws on new quantitative and qualitative data from 4 homes involved in a smart home field trial that have been equipped with smart home systems that provide advanced control functionality over appliances and space heating. Quantitative data examines how householders have used the systems both to try and improve their energy efficiency but also for purposes such as enhanced security or scheduling appliances to align with lifestyles. Qualitative data (from in-depth interviews) explores how smart technologies have impacted upon, and were impacted by, broader understandings of control within the home. The paper concludes by proposing an analytical framework for future research on control in the smart home.

https://ueaeprints.uea.ac.uk/id/document/15080

Tom Kane

Papers

Summertime temperatures and thermal comfort in UK homes

How are UK homes heated? A city-wide, socio-technical survey and implications for energy modelling

A data management platform for personalised real-time energy feedback

Smart homes, control and energy management: How do smart home technologies influence control over energy use and domestic life?

Heating behaviour in English homes: An assessment of indirect calculation methods