Results 1 - 10
of
21
Conversations with my Washing Machine: An in-thewild Study of Demand Shifting with Self-generated Energy
- In Proc. UbiComp (Seattle
, 2014
"... Domestic microgeneration is the onsite generation of low-and zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine – that of doing laundry – can be augmented by digital technologies to help households with photovolt ..."
Abstract
-
Cited by 5 (4 self)
- Add to MetaCart
(Show Context)
Domestic microgeneration is the onsite generation of low-and zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine – that of doing laundry – can be augmented by digital technologies to help households with photovoltaic solar energy generation to make better use of self-generated energy. This paper presents an 8-month in-the-wild study that involved 18 UK households in longitudinal energy data collection, prototype deployment and participatory data analysis. Through a series of technology interventions mixing energy feedback, proactive suggestions and direct control the study uncovered opportunities, potential rewards and barriers for families to shift energy consuming household activities and highlights how digital technology can act as mediator between household laundry routines and energy demand-shifting behaviors. Finally, the study provides insights into how a “smart ” energy-aware washing machine shapes organization of domestic life and how people “communicate ” with their washing machine. Author Keywords Microgeneration; domestic computing; sustainability; user studies ACM Classification Keywords H.5.2. Information interfaces and presentation (e.g., HCI):
Forecasting Multi-Appliance Usage for Smart Home Energy Management
"... We address the problem of forecasting the usage of multiple electrical appliances by domestic users, with the aim of providing suggestions about the best time to run appliances in order to reduce car-bon emissions and save money (assuming time-of-use pricing), while minimising the impact on the user ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
(Show Context)
We address the problem of forecasting the usage of multiple electrical appliances by domestic users, with the aim of providing suggestions about the best time to run appliances in order to reduce car-bon emissions and save money (assuming time-of-use pricing), while minimising the impact on the users ’ daily habits. An important challenge related to this problem is the modelling the everyday rou-tine of the consumers and of the inter–dependencies between the use of different appliances. Given this, we develop an important building block of future home energy management systems: a prediction al-gorithm, based on a graphical model, that captures the everyday habits and the inter–dependency be-tween appliances by exploiting their periodic fea-tures. We demonstrate through extensive empiri-cal evaluations on real–world data from a promi-nent database that our approach outperforms exist-ing methods by up to 47%. 1
Activity Prediction for Agent-based Home Energy Management
"... In this paper, we address the problem of predicting the usage of home appliances where a key challenge is to model the ev-eryday routine of homeowners and the inter–dependency be-tween the use of different appliances. In particular, given an efficient day–ahead prediction of electrical usage, home e ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
In this paper, we address the problem of predicting the usage of home appliances where a key challenge is to model the ev-eryday routine of homeowners and the inter–dependency be-tween the use of different appliances. In particular, given an efficient day–ahead prediction of electrical usage, home en-ergy management systems can suggest homeowners when is the best time to run appliances in order to save cost, without violating their preferred everyday habits. To this end, we propose an agent based prediction algorithm that captures the everyday habits by exploiting their periodic features. In addition, our algorithm uses a episode generation hid-den Markov model (EGH) to model the inter–dependency between appliances. We demonstrate that our approach outperforms existing methods by up to 40 % in experiments based on real–world data from a prominent database of home energy usage. We also show that the computational cost of our algorithm is 100 times lower on average, compared to that of the benchmark algorithms. 1.
Exploring the hidden impacts of HomeSys: energy and emissions of home sensing and automation
- in UbiComp’13
"... Abstract Home sensing and automation systems are rarely discussed with reference to their direct energy demand, much less other environmental impacts such as greenhouse gas (GhG) emissions arising from their manufacture and transport. It is imperative that designers of such systems understand the i ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract Home sensing and automation systems are rarely discussed with reference to their direct energy demand, much less other environmental impacts such as greenhouse gas (GhG) emissions arising from their manufacture and transport. It is imperative that designers of such systems understand the impacts of the technologies they introduce, particularly where intended to save energy and promote sustainability. Using four case studies drawn from recent Ubicomp and HCI literature, this reflective paper quantifies the direct energy and estimates the embodied emissions arising from specific installations of home sensing. We contextualise this by comparing with typical impacts arising from existing ICT devices commonly found in the home, and highlight a number of ways in which designers can reduce the impacts of the systems they introduce into the home.
Using Rule Mining to Understand Appliance Energy Consumption Patterns
"... Abstract—Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behav ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Abstract—Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25 % of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70 % of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes. I.
Sustainability Begins in the Street:
"... Abstract—Recent scholarship in sustainable HCI has called for research beyond change at the individual level. This paper aims to contribute to research on community-level sustainable practices. We report an ethnographic study of the Transition movement, a global social movement encouraging sustainab ..."
Abstract
- Add to MetaCart
Abstract—Recent scholarship in sustainable HCI has called for research beyond change at the individual level. This paper aims to contribute to research on community-level sustainable practices. We report an ethnographic study of the Transition movement, a global social movement encouraging sustainability. We discuss how Transition movement participants in Totnes, UK mobilized community resources, developing a shared moral sense about sustainability, and undertaking positive, collective, community actions. We discuss how sustainable HCI can engage community-level practices. Keywords—sustainable HCI; sustainability; community;
Energy Advisors at Work: Charity Work Practices to Support People in Fuel Poverty
"... ABSTRACT We present an ethnographic study of energy advisors working for a charity that provides support, particularly to people in fuel poverty. Our fieldwork comprises detailed observations that reveal the collaborative, interactional work of energy advisors and clients during home visits, supple ..."
Abstract
- Add to MetaCart
(Show Context)
ABSTRACT We present an ethnographic study of energy advisors working for a charity that provides support, particularly to people in fuel poverty. Our fieldwork comprises detailed observations that reveal the collaborative, interactional work of energy advisors and clients during home visits, supplemented with interviews and a participatory design workshop with advisors. We identify opportunities for Ubicomp technologies that focus on supporting the work of the advisor, including complementing the collaborative advice giving in home visits, providing help remotely, and producing evidence in support of accounts of practices and building conditions useful for interactions with landlords, authorities and other third parties. We highlight six specific design challenges that relate the domestic fuel poverty setting to the wider Ubicomp literature. Our work echoes a shift in attention from energy use and the individual consumer, specifically to matters of advice work practices and the domestic fuel poverty setting, and to the discourse around inclusive Ubicomp technologies.
Computational Environmental Ethnography: Combining Collective Sensing and Ethnographic Inquiries to Advance Means for Reducing Environmental Footprints
"... ABSTRACT We lack an understanding of human values, motivations and behavior in regards to new means for changing people's behavior towards more sustainable choices in their everyday life. Previous anthropological and sociological studies have identified these objects of study to be quite compl ..."
Abstract
- Add to MetaCart
(Show Context)
ABSTRACT We lack an understanding of human values, motivations and behavior in regards to new means for changing people's behavior towards more sustainable choices in their everyday life. Previous anthropological and sociological studies have identified these objects of study to be quite complex and to require new methods to be unfolded further. Especially behavior within the privacy of people's homes has proven challenging to uncover through the use of traditional qualitative and quantitative social scientific methods (e.g. interviews, participatory observations and questionnaires). Furthermore, many research experiments are attempting to motivate environmental improvements through feedback via, e.g., room displays, web pages or smart phones, based on (smart) metering of energy usage, or for saving energy by automatic control of, e.g., heating, lighting or appliances. However, existing evaluation methods are primarily unilateral by opting for either a quantitative or a qualitative method or for a simple combinationand therefore do not provide detailed insight into the potentials and impacts of such solutions. This paper therefore proposes a combined quantitative and qualitative collective sensing and anthropologic investigation methodology we term Computational Environmental Ethnography, which provides quantitative sensing data that document behavior while facilitating qualitative investigations to link the data to explanations and ideas for further sensing. We propose this methodology to include the establishment of base lines, comparative experimental feedback, traceable sensor data with respect for different privacy levels, visualization of sensor data, qualitative explanations of recurrent and exceptional patterns in sensor data, taking place as part of an innovative process and in an iterative interplay among complementing disciplines, potentially including also partners from industry. Experiences from using the methodology in a zero-emission home setting, as well as an ongoing case investigating transportation habits are discussed.
HomeFlow: Inferring Device Usage with Network Traces ACM Classification Keywords
"... Abstract Previous studies in home energy have taken a service oriented approach to disaggregating direct energy consumption. With a particular focus on media and ICT services in the home, our proposed platform builds upon this work by providing activity oriented data, collected through home network ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract Previous studies in home energy have taken a service oriented approach to disaggregating direct energy consumption. With a particular focus on media and ICT services in the home, our proposed platform builds upon this work by providing activity oriented data, collected through home network monitoring. This information will be used to build a profile of communication between devices. This includes inter-device communication within the confines of a home environment, and also the use of external resources outside of the home. This provides knowledge of device behaviour and enables profiling of device relationships. Furthermore, monitoring communication to locations outside of the home will enable us to estimate associated indirect energy costs. These are incurred when a user consumes an externally provided service, such as Video-on-Demand.
Placing information at home: Using room context in domestic design
"... Abstract Residential and commercial buildings are responsible for about 40% of the EU's total energy consumption ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract Residential and commercial buildings are responsible for about 40% of the EU's total energy consumption
