20 research outputs found
PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring
Non-intrusive presence detection of individuals in commercial buildings is
much easier to implement than intrusive methods such as passive infrared,
acoustic sensors, and camera. Individual power consumption, while providing
useful feedback and motivation for energy saving, can be used as a valuable
source for presence detection. We conduct pilot experiments in an office
setting to collect individual presence data by ultrasonic sensors, acceleration
sensors, and WiFi access points, in addition to the individual power monitoring
data. PresenceSense (PS), a semi-supervised learning algorithm based on power
measurement that trains itself with only unlabeled data, is proposed, analyzed
and evaluated in the study. Without any labeling efforts, which are usually
tedious and time consuming, PresenceSense outperforms popular models whose
parameters are optimized over a large training set. The results are interpreted
and potential applications of PresenceSense on other data sources are
discussed. The significance of this study attaches to space security, occupancy
behavior modeling, and energy saving of plug loads.Comment: BuildSys 201
Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis
We describe a social game that we designed for encouraging energy efficient
behavior amongst building occupants with the aim of reducing overall energy
consumption in the building. Occupants vote for their desired lighting level
and win points which are used in a lottery based on how far their vote is from
the maximum setting. We assume that the occupants are utility maximizers and
that their utility functions capture the tradeoff between winning points and
their comfort level. We model the occupants as non-cooperative agents in a
continuous game and we characterize their play using the Nash equilibrium
concept. Using occupant voting data, we parameterize their utility functions
and use a convex optimization problem to estimate the parameters. We simulate
the game defined by the estimated utility functions and show that the estimated
model for occupant behavior is a good predictor of their actual behavior. In
addition, we show that due to the social game, there is a significant reduction
in energy consumption
Real-time model-based fault diagnosis for switching power converters
Abstract—We present the analysis, design, and experimental implementation of a fault diagnosis method for switching power converters using a model-based estimator approach. The fault diagnosis method enables efficient detection and identification of component and sensor faults, and is implemented on the same computation platform as the control system. The model-based estimator operates in parallel with the switching power converter, and generates an error residual vector that can be used to detect and identify particular component or sensor faults. This paper presents an experimental demonstration for a 1.2 kW rack-level uninterruptable power supply (UPS) dc-dc converter for data center applications. Simulation and experimental results demonstrate fault detection and identification for various com-ponent and sensor faults in the converter. Moreover, we show that the proposed fault diagnosis design and analysis methods are applicable to a broad class of converter topologies and fault types. I
A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks
Energy game-theoretic frameworks have emerged to be a successful strategy to
encourage energy efficient behavior in large scale by leveraging
human-in-the-loop strategy. A number of such frameworks have been introduced
over the years which formulate the energy saving process as a competitive game
with appropriate incentives for energy efficient players. However, prior works
involve an incentive design mechanism which is dependent on knowledge of
utility functions for all the players in the game, which is hard to compute
especially when the number of players is high, common in energy game-theoretic
frameworks. Our research proposes that the utilities of players in such a
framework can be grouped together to a relatively small number of clusters, and
the clusters can then be targeted with tailored incentives. The key to above
segmentation analysis is to learn the features leading to human decision making
towards energy usage in competitive environments. We propose a novel graphical
lasso based approach to perform such segmentation, by studying the feature
correlations in a real-world energy social game dataset. To further improve the
explainability of the model, we perform causality study using grangers
causality. Proposed segmentation analysis results in characteristic clusters
demonstrating different energy usage behaviors. We also present avenues to
implement intelligent incentive design using proposed segmentation method.Comment: Proceedings of the Special Session on Machine Learning in Energy
Application, International Conference on Machine Learning and Applications
(ICMLA) 2019. arXiv admin note: text overlap with arXiv:1810.1053
Diagnosing and predicting wind turbine faults from SCADA data using support vector machines
Unscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. To date, a strong effort has been applied to developing Condition Monitoring Systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine's Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, fault and alarm data from a turbine on the Southern coast of Ireland is analysed to identify periods of nominal and faulty operation. Classification techniques are then applied to detect and diagnose faults by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show recall scores generally above 80\% for fault detection and diagnosis, and prediction up to 24 hours in advance of specific faults, representing significant improvement over previous techniques