A convex optimization approach for automated water and energy end use disaggregation

A detailed knowledge of water consumption at an end-use level is an essential requirement to design and evaluate the efficiency of water saving policies. In the last years, this has led to the development of automated tools to disaggregate high resolution water consumption data at the household level into end use categories. In this work, a new disaggregation algorithm is presented. The proposed algorithm is based on the assumption that the disaggregated signals to be identified are piecewise constant over the time and it exploits the information on the time-of-day probability in which a specific water use event might occur. The disaggregation problem is formulated as a convex optimization problem, whose solution can be efficiently computed through numerical solvers. Specifically, the disaggregation problem is treated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piece-wise constant over the time. The proposed disaggregation algorithm has been initially tested against household electricity data available in the literature. The obtained results look promising and similar results are expected to be obtained for water data

Benefits and challenges of using smart meters for advancing residential water demand modeling and management: a review

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world

Updated version of final design and of the architecture of SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Modelling residential water consumers’ behaviors by feature selection and feature weighting

Identifying the most relevant determinants of water consuming or saving behaviors at the household level is key to building mathematical models that predict urban water demand variability in space and time and to explore the effects of different Water Demand Management Strategies for the residential sector. This work contributes a novel approach based on feature selection and feature weighting to model the single-user consumption behavior at the household level. A two-step procedure consisting of the extraction of the most relevant determinants of users’ consumption and the identification of a predictive model of water consumers’ profile is proposed and tested on a real case study. Results show the effectiveness of the proposed method in capturing the influence of candidate determinants on residential water consumption, as well as in attaining sufficiently accurate predictions of users’ consumption profiles, which constitutes essential information to support residential water demand management

DROP and FUNERGY: Two gamified learning projects for water and energy conservation

DROP! is a card game developed by the SmartH2O research consortium, funded by the European FP7 Program. Its goal is to engage people in using a gamified platform which offers a wide set of activities aimed at triggering water saving actions. The game has not been designed as a learning tool but a sort of "fun bridge" to enter the SmartH2O gamified portal and discover how to reduce our daily water consumption. Based on the water saving results achieved with SmartH2O (up to 21% reduction in the case study performed in the city of Valencia) the transfer of the successful approach to the energy domain is being undertaken in a new European project named enCOMPASS, which aims at developing solutions that enable users to save energy without causing a negative impact on their daily quality of life. The project is developing new tools to collect and visualize energy consumption data and to provide personalized recommendations for energy saving in test areas involving schools and public buildings. In conjunction to the tools, a game will also be developed: FUNERGY. In this contribution we present the game concept and lessons learned from the DROP! game for water saving and the first ideas for its adaptation to the energy domain

Effect of music-based multitask training on cognition and mood in older adults

Background: in a secondary analysis of a randomised controlled trial, we investigated whether 6 months of music-based multitask training had beneficial effects on cognitive functioning and mood in older adults. Methods: 134 community-dwellers aged ≥65 years at increased risk for falling were randomly assigned to either an intervention group (n = 66) who attended once weekly 1-h supervised group classes of multitask exercises, executed to the rhythm of piano music, or a control group with delayed intervention (n = 68) who maintained usual lifestyle habits, for 6 months. A short neuropsychological test battery was administered by an intervention-blinded neuropsychologist at baseline and Month 6, including the mini-mental state examination (MMSE), the clock-drawing test, the frontal assessment battery (FAB) and the hospital anxiety (HADS-A) and depression scale. Results: intention-to-treat analysis showed an improvement in the sensitivity to interference subtest of the FAB (adjusted between-group mean difference (AMD), 0.12; 95% CI, 0.00 to 0.25; P = 0.047) and a reduction in anxiety level (HADS-A; AMD, −0.88; 95% CI, −1.73 to −0.05; P = 0.039) in intervention participants, as compared with the controls. Within-group analysis revealed an increase in MMSE score (P = 0.004) and a reduction in the number of participants with impaired global cognitive performance (i.e., MMSE score ≤23; P = 0.003) with intervention. Conclusion: six months of once weekly music-based multitask training was associated with improved cognitive function and decreased anxiety in community-dwelling older adults, compared with non-exercising controls. Studies designed to further delineate whether training-induced changes in cognitive function could contribute to dual-task gait improvements and falls reduction, remain to be conducte

An optimisation-based energy disaggregation algorithm for low frequency smart meter data

An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives

Challenges in Visual Anomaly Detection for Mobile Robots

We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.Comment: Workshop paper presented at the ICRA 2022 Workshop on Safe and Reliable Robot Autonomy under Uncertainty https://sites.google.com/umich.edu/saferobotautonomy/hom