A Social-Centred Gamification Approach to Improve Household Water Use Efficiency

The research community is showing a growing interest in gamification and there are works showing the usefulness of gamification in different problem domains. Recently, a special interest has been given to the gamification design on systems addressing natural resource consumption issues such as to encourage efficient household water consumption. Despite the potential benefits, the gamification design method for such system is not conclusive. In this paper, we proposed a social-centred gamification approach to improve household water use efficiency. The approach firstly identified the water use related social network activities based upon existing popular social network activities. The approach then gamified each identified activity in terms of traditional instruments for improving water use efficiency and gamification rewards. The approach also used a set of indicators to explicitly detect and monitor both online social network activities and offline water use activities. With this approach the gamification effectiveness can be better traced and evaluated.ISS-EWATUS, Integrated Support System for Efficient Water Usage and Resources Management, FP7 project (grant no. 619228), funded by the European Communit

Unsupervised monitoring of an elderly person\u27s activities of daily living using Kinect sensors and a power meter

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored. Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly. To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel. A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value. Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns. As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours. The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs

Algorithms for appliance usage prediction

Demand-Side Management (DSM) is one of the key elements of future Smart Electricity Grids. DSM involves mechanisms to reduce or shift the consumption of electricity in an attempt to minimise peaks. By so doing it is possible to avoid using expensive peaking plants that are also highly carbon emitting. A key challenge in DSM, however, is the need to predict energy usage from specific home appliances accurately so that consumers can be notified to shift or reduce the use of high energy-consuming appliances. In some cases, such notifications may be also need to be given at very short notice. Hence, to solve the appliance usage prediction problem, in this thesis we develop novel algorithms that take into account both users' daily practices (by taking advantage of the cyclic nature of routine activities) and the inter-dependency between the usage of multiple appliances (i.e., the user's typical consumption patterns). We propose two prediction algorithms to satisfy the needs for fast prediction and high accuracy respectively: i) a rule-based approach, EGH-H, for scenarios in which notifications need to be given at short notice, to find significant patterns in the use of appliances that can capture the user's behaviour (or habits), ii) a graphical{model based approach, GM-PMA (Graphical Model for Prediction in Multiple Appliances) for scenarios that require high prediction accuracy. We demonstrate through extensive empirical evaluations on real{world data from a prominent database of home energy usage that GM-PMA outperforms existing methods by up to 41%, and the runtime of EGH-H is 100 times lower on average, than that of other benchmark algorithms, while maintaining competitive prediction accuracy. Moreover, we demonstrate the use of appliance usage prediction algorithms in the context of demand{side management by proposing an Intelligent Demand Responses (IDR) mechanism, where an agent uses Logistic Inference to learn the user's preferences, and hence provides the best personalised suggestions to the user. We use simulations to evaluate IDR on a number of user types, and show that, by using IDR, users are likely to improve their savings significantly

Sensitivity of water meters to small leakage

Abstract Water leakage beyond the meter at the household level is becoming an emerging problem in a world where water must be respected and saved. More than public awareness campaigns for citizens, automatic leakage detection could give in the future the best results. Domestic water consumption will be continuously monitored by smart meters able to distinguish between normal absorption and leakage. Nowadays, some research prototypes of smart water meters were designed for continuous monitoring aimed to collect measurements and send them to a central unit for developing statistics on consumptions and alarms. In this paper, the authors propose a battery-powered visual smart device that could be a good starting point to generate leakage alarms at the household level. After a brief description of state of the art, the paper at first faces the problem of the leakage detection dependence on meter sensitivity. Then, an image-based technique for automatic "null consumption detection" to be applied both to the register last digit and to a needle of water meters is tested on three different water meters. Finally, experimental results confirm that this image-based technique, allowing the automatic detection of Periods With Null Consumption, can be very useful for water leakage detection algorithms

Who Produces the Peaks? Household Variation in Peak Energy Demand for Space Heating and Domestic Hot Water

Extensive research demonstrates the importance of user practices in understanding variations in residential heating demand. Whereas previous studies have investigated variations in aggregated data, e.g., yearly heating consumption, the recent deployment of smart heat meters enables the analysis of households’ energy use with a higher temporal resolution. Such analysis might provide knowledge crucial for managing peak demand in district heating systems with decentralized production units and increased shares of intermittent energy sources, such as wind and solar. This study exploits smart meter heating consumption data from a district heating network combined with socio-economic information for 803 Danish households. To perform this study, a multiple regression analysis was employed to understand the correlations between heat consumption and socio-economical characteristics. Furthermore, this study analyzed the various households’ daily profiles to quantify the differences between the groups. During an average day, the higher-income households consume more energy, especially during the evening peak (17:00–20:00). Blue-collar and unemployed households use less during the morning peak (5:00–9:00). Despite minor differences, household groups have similar temporal patterns that follow institutional rhythms, like working hours. We therefore suggest that attempts to control the timing of heating demand do not rely on individual households’ ability to time-shift energy practices, but instead address the embeddedness in stable socio-temporal structures

Smart Energy Management for Households:

The aim of the research presented in this thesis was to infer design-related insights and guidelines to improve the use and effectiveness of home energy management systems (HEMS). This was done through an empirical evaluation of the longitudinal effectiveness of these devices and an exploration of factors that influence their use and effectiveness. Three case studies executed with three different HEMS in households, a life cycle assessment (LCA) on those three HEMS, as well as a reflection on the challenges of both researching and implementing HEMS in existing housing gave a comprehensive picture of the opportunities and barriers for HEMS. The research revealed five typical use patterns that emerged amongst households. It also revealed average energy savings of 7.8%, which however decreased in the follow-up that was conducted, and factors that may influence the use and effectiveness of HEMS. Nonetheless, the LCA calculations divulged that the HEMS can achieve net energy savings when taking their embedded energy into account. Problem statement The goal of reducing the energy consumption of existing housing formed the basis for this research. There are many facets to this energy consumption, including the characteristics of the house, its appliances, and the behaviours of its inhabitants. Because of this complexity, addressing only one of these facets is not effective in substantially reducing the overall energy consumption of households. This called for an interdisciplinary approach, merging the domains of design for sustainability, sustainable housing transformation and environmental psychology. In this thesis, HEMS were chosen as the intervention to address the various elements that contribute to household energy consumption, thereby functioning as a pivot. By giving feedback and/or helping manage consumption they can assist households in changing their behaviour and help save energy. However, in analysing literature on HEMS, four critique points were encountered. Past research tends to be limited in the types of HEMS and energy sources studied. Furthermore, limited knowledge was available on the longitudinal effectiveness of HEMS, the large variances in achieved energy savings and use of HEMS, and factors influencing their use and effectiveness. Conceptual framework To address these critique points and explore the influence of the factors: user, HEMS, other people, other products, context, and time; a framework was proposed. It postulated the pivotal role of HEMS and visualized the interdependence of the different elements. This framework structured the findings of the research. Case studies Three case studies were conducted. The first case study with an electricity monitor revealed that the effectiveness of HEMS tends to decrease over time. The initial savings in electricity consumption of, on average, 7.8% after four months were not sustained over a period of 15 months. The participants were divided into three groups, who all had the same rate of fall-back. However, the group that had a daily habit after 15 months of checking the monitor, achieved the largest savings in comparison to the groups that did not have a daily habit after 15 months or that returned the monitor after four months. The second and third case study with a multifunctional HEMS and a energy management device revealed five distinctive use patterns: there was often one main user who varied strongly in their knowledge of, interest in, and affinity with energy and technique in general, resulting in different needs and desires concerning the HEMS. Additionally, the studies revealed that contextual factors, such as the structure of the home and its energy meters, can impede the use and implementation of HEMS. Lifecycle assessment The positive result of the LCA was that all three types of HEMS can theoretically achieve net energy savings (where einvested < esaved) over the course of five years in the six scenarios that were created. However, it can take up to 24 months to achieve net energy savings, depending on the scenario and type of HEMS. No HEMS achieve a positive return on investment within five years in all six scenarios. Conclusions This research found that the role of HEMS in reducing the energy consumption of households is constrained when not taking the factors into consideration that were distilled in the case studies. For one, human factors, such as the characteristics of the user and other household members and family dynamics, may influence the use and effectiveness. Furthermore, physical elements, such as the design of the HEMS (e.g., the type of feedback, the quality of the technique, its usability and applicability) the design and functioning of appliances and the dwelling played a role. Particularly, the interplay between people and these physical elements such as the match/mismatch or compatibility/incompatibility between HEMS, users, appliances and the dwelling were influential, in part due to the complexity of reducing energy consumption and users’ preferred type of reduction approach. Based on these factors, design guidelines where formulated for HEMS with the aim to achieve lasting energy savings and increase the usability of HEMS. Examples of these guidelines are: HEMS should not be developed as standalone interventions but should be incorporated as part of a broader, overarching change strategy; one size does not fit all; and careful trade-offs needs to be made with regard to the design of the HEMS, e.g., while a small display size is positive for the LCA, it could limit the potential to influence behaviour. Relevance The resulting knowledge of these studies can be employed to inspire the different domains merged within this thesis. For the HEMS industry: in striving to designing HEMS that are capable of influencing users, effective in reducing energy consumption, and easily usable and implementable in everyday life. For the building industry this research illustrated the benefit of considering the behaviour of inhabitants in achieving sustainable housing transformation. Furthermore, lessons were presented in how the building industry can contribute to increasing the ease of implementation of HEMS. HEMS researchers may assimilate knowledge for future research to deepen the knowledge on ways of increasing the effectiveness of HEMS

Modelling and Predicting Energy Usage from Smart Meter Data and Consumer behaviours in Residential Houses.

Efforts of electrical utilities to respond to climate change requires the development of increasingly sophisticated, integrated electrical grids referred to as the smart grids. Much of the smart grid effort focuses on the integration of renewable generation into the electricity grid and on increased monitoring and automation of electrical transmission functions. However, a key component of smart grid development is the introduction of the smart electrical meter for all residential electrical customers. Smart meter deployment is the corner stone of the smart grid. In addition to adding new functionality to support system reliability, smart meters provide the technological means for utilities to institute new programs to allow their customers to better manage and reduce their electricity use and to support increased renewable generation to reduce greenhouse emissions from electricity use. As such, this thesis presents our research towards the study of how the data (energy usage profiles) produced by the smart meters within the smart grid system of residential homes is used to profile energy usage in homes and detect users with high fuel consumption levels. This project concerns the use of advanced machine learning algorithms to model and predict household behaviour patterns from smart meter readings. The aim is to learn and understand the behavioural trends in homes (as demonstrated in chapter 5). The thesis shows the trends of how energy is used in residential homes. By obtaining these behavioural trends, it is possible for utility companies to come up with incentives that can be beneficial to home users on changes that can be adopted to reduce their carbon emissions. For example consumers would be more likely prompted to turn of unusable appliances that are consuming high energy around the home e.g., lighting in rooms which are un occupied. The data used for the research is constructed from a digital simulation model of a smart home environment comprised of 5 residential houses. The model can capture data from this simulated network of houses, hence providing an abundance set of information for utility companies and data scientist to promote reductions in energy usage. The simulation model produces volumes of outliers such as high periods (peak hours) of energy usage and low periods (Off peak hours) of anomalous energy consumption within the residential setting of five homes. To achieve this, performance characteristics on a dataset comprised of wealthy data readings from 5 homes is analysed using Area under ROC Curve (AUC), Precision, F1 score, Accuracy and Recall. The highest result is achieved using the Two-Class Decision Forest classifier, which achieved 87.6% AUC