Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Bovine Respiratory Disease (BRD) is the second leading cause of death in young dairy calves, and is associated with less growth, and reduced long-term performance such as less milk production, which makes BRD a financial burden on a farm’s economy. Precision technologies, such as accelerometers, automatic feeders, and cameras have been extensively used to collect, summarize, and interpret changes in baseline dairy cattle behavior. While some efforts to evaluate the presence of statistical relationships between calves’ behavior and BRD status have been made, there is very little research in pairing such technologies with manual examinations to improve the accuracy and cost of BRD monitoring. In this paper, we propose a framework for diagnosis and early prediction of BRD in calves. This framework is composed by a machine learning model as well as by a cost-sensitive feature selection problem called Cost Optimization Worth (COW). COW maximizes prediction accuracy given a budget constraint. We show that COW is NP-Hard and propose an efficient heuristic with polynomial complexity. We validate our methodology on a real dataset of 46 automatic and manually collected features, representing 106 calves observed during the preweaning period of 50 days. Our results show that our machine learning model can correctly classify a sick cow with a 97% accuracy and up to 5 days prior to BRD diagnosis, outperforming a recent state-of-the-art approach. Furthermore, our feature selection results show that in a low-budget scenario, manually collected features are more valuable than automated features in detecting sick cows. Conversely, in a high-budget scenario, automated features report higher accuracy for the early prediction of BRD.

Prolonged rotating outages and exorbitant energy bills, recently experienced in California and Texas, have exposed the limitations and need for modernizing electric power systems. The occurrence of such events is a consequence of peak loads, often due to extreme outside temperatures that simultaneously trigger Heating Ventilation Air Conditioning (HVAC) systems. Leveraging pervasive computing technologies, such as smart meters and smart thermostats, this paper introduces a comprehensive approach to perform residential HVAC power conservation and prevent these catastrophic events. Differently from previous solutions, our approach models realistic user behavior and HVAC dynamics of individual homes. Specifically, we formulate a novel reverse auction-based problem, called POwer Conservation Optimization (POCO). The goal is to perform power conservation by motivating users to temporarily adjust their HVAC thermostat settings in exchange for financial rewards. We prove that POCO ensures truthfulness and individual rationality of the auction mechanism, although it is an NP-hard problem. Therefore, we propose an efficient heuristic, called Greedy Ranking AllocatioN (GRAN), which we prove ensures the same formal properties, while incurring only a polynomial complexity. To predict power savings resulting from an HVAC thermostat adjustments, we propose a novel machine learning-based technique called Power Saving Prediction (PSP). In addition, we conduct an online survey to study the willingness to adopt the proposed system and to model realistic user behavior. Survey results show willingness of adoption above 79% and a highly heterogeneous and non-linear user behavior. We perform extensive experiments using high-fidelity simulator EnergyPlus. Results show that PSP outperforms a state-of-the-art solution obtaining 85% predictions within a 5% error margin. Furthermore, GRAN achieves near-optimal performance, outperforming a recent state-of-the-art approach obtaining results between 58% and 68% closer to the optimum.

The growth and widespread diffusion of Internet-of-Things devices and advanced metering infrastructure allows to closely monitor appliances in a user home. Although only few works have focused on the issue of individual home power consumption predictions, recent efforts have unveiled the complexity of this task. As opposed to building-level power predictions, the finer granularity of single home predictions is characterized by the high impact that individual user actions have on the power consumption. As a matter of fact, the current state of the art shows inadequate prediction performance. In this work, we investigate the issue of single home power prediction by analyzing a recent dataset of real power consumption data. We carry out a profound analysis of several processing parameters and environmental parameters that make this task so challenging, thus providing meaningful insights that can guide future research on individual home power consumption predictions. Results show an overall low daily error, and very accurate hourly predictions when less variable usage patterns occur.

Abstract Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.

Activity recognition has been introduced as a means of detecting an action from a series of observations. Although in the literature, the terms "activity recognition" and "human activity recognition" are mostly used interchangeably, there exist several interesting applications for non-human subjects. In this work, we study animal activity recognition with special focus on horse gaits recognition. The on-body and unobtrusive system developed in this paper has several potential applications which can raise attention towards financial, emotional and veterinary health issues related to animals. Leveraging the pervasive use of smartwatches for activity tracking, we develop a smartwatch application to collect accelerometer data. The application is based on novel outlier detection and feature extraction techniques, in conjunction with state-of-the-art machine learning approaches. We perform real life experiments with two horses to evaluate the performances of our proposed system. To this aim, we place the monitoring device both on the horse saddle and the rider's wrist. The results show a high accuracy in both scenarios, which allows a seamless and unobtrusive use of our wearable device application by the rider. In addition, we study the effects of sliding window size and sampling frequency, providing useful insights for future research in horse gaits recognition.