Abstract: Systems and methods of determining sub-tree similarity for component substitution. A method includes assigning a similarity metric to a plurality of trees stored in computer-readable media. The method also includes constructing a distance matrix in computer-readable media, the distance being between sub-trees. The method also includes correlating sub-trees in the computer-readable media based on the distance matrix.

Abstract: A method is provided for system deconstruction for component substitution. The method includes a system tree which is deconstructed in a computer-readable medium, the system tree deconstructing into constituent nodes. Each node in the system tree represents a characteristic of a component of a system under consideration. A database of trees is searched for trees containing similar attributes to the system tree. Trees are qualified based on a frequency of nodes that are similar to certain highly ranked nodes in the system tree. The most relevant nodes from the qualified trees are selected. Searching the database is terminated when the most relevant nodes account for a fraction, less than a whole, of an environmental footprint of the system tree.

Abstract: Example embodiments relate to providing visual analytics of spatial time series data. In example embodiments, sensors may be located at regions within a building for collecting sensor data at regular time intervals. A sensor hierarchy can be generated including sensor nodes that are hierarchically arranged according to a physical infrastructure of the building, where each of the sensor nodes corresponds to a sensor. Sensor data can be obtained from the sensors, and a pixel calendar tree can be generated based on the sensor data and the sensor hierarchy, where the pixel calendar tree is recursively subdivided into tree portions according to a proportion of the sensor data attributable to each of the sensors. The pixel calendar tree can be displayed, where each of the tree portions includes time series sensor data of a corresponding region that is generated based on the sensor data.

Abstract: A method for determining regions of influence of a plurality of fluid moving devices in an infrastructure is provided. In the method, a plurality of clusters are generated, with each of the plurality of clusters containing a representation of fluid supply data pertaining to a fluid moving device and one or more of the plurality of sensors based upon collected sensor and fluid supply data. In addition, regions of influence of each of the plurality of fluid moving devices are identified from an analysis of the generated clusters.

Abstract: Visual structures are produced to represent corresponding motifs, where the motifs correspond to repeating patterns in an input data set, where sizes of the visual structures are based on values of a characteristic associated with the motifs, and where a first of the motifs is nested within a second of the motifs to represent a hierarchical relationship of the patterns represented by the first and second motifs. The visual structures of corresponding motifs are presented for display in a visualization screen, where the visualization screen depicts the data in the input data set with the visual structures overlapping parts of depicted data. Interactive input is received specifying modification of a region containing motifs, wherein the specified modification includes interactive distortion of the visual structures in the region to change a size of the visual structures in the region.

Abstract: Examples of meter data management testing are disclosed. In one example implementation according to aspects of the present disclosure, a computing device may include one or more processors, a memory for storing machine readable instructions, and a data store. The computing device may further include a meter data management testing module stored in the memory and executing on at least one of the one or more processors to test a meter data management system using a data set of representative metering data and a user-generated test scenario specifying a plurality of metering parameters.

Abstract: Determining sensor placement in distributed networks includes identifying measurement locations in a distributed network with a tree topology and selecting a subset of the measurement locations for sensor placement with a mutual information function.

Abstract: Storing time series data for a search query includes identifying a time series whose representation is to be pre-computed based on available memory storage, pre-computing at least one representation of the identified time series, and storing the at least one representation in the memory storage.

Abstract: Described herein are techniques for identifying anomalous behavior of a monitored entity. Features can be extracted from data related to operation of an entity. The features can be mapped to a plurality of states to generate a state sequence. An observed value of a metric can be compared to an expected value of the metric based on the state sequence.

Abstract: A method for detecting power consumption anomalies. The method obtains two different time power series and compares them to generate a dissimilarity matrix. A dimensionality reduction equation is applied to the dissimilarity matrix to obtain a low dimensional embedding of the power data. A probability score of each of the time series being anomalous is then calculated.

Abstract: According to an example, synthetic time series data generation may include receiving time series data for a plurality of users, and applying dimensionality reduction to transform the time series data from a high dimensional space n of the time series data to a low dimensional space m, where m<n. The transformed time series data may be used to estimate a time series data density in the low dimensional space m by generating a density function. The density function may be sampled for data, and the sampled data may be transformed back to the high dimensional space n to generate the synthetic time series data.

Abstract: Visual analytics using multivariate concentric rings with a visual start time mechanism includes displaying an interactive graph where the interactive graph has multiple concentric rings that have multiple cells that represent sequential time periods. The concentric rings form a time unit that starts at an origin and ends at a time unit end and also has a pre-nonorigin starting section and a post-nonorigin starting section. A color is displayed in the cells to represent measurements associated with time stamps corresponding to cells in the post-nonorigin starting section. Further, a background color is displayed in cells of the pre-nonorigin starting section. The cells in the pre-nonorigin starting section are reused by displaying a color to represent metrics associated with time stamps belonging to a subsequent time unit.

Abstract: Characterizing risks in resource distribution systems includes collecting measurements from multiple end users in a resource distribution system and identifying a subset of the end users that pose a greater than average risk to the resource distribution system with a risk identification engine.

Abstract: Detecting fraud in resource distribution systems includes determining a meter of a resource in a resource distribution system exhibits a characteristic indicative of fraud and increasing a collection frequency of measurements of a usage of the resource of the meter per unit of time.

Abstract: In an implementation, a method for managing an entity may include establishing of a framework for characterization of the entity, in which the framework abstracts operation of the entity to a state machine. In addition, each state in the state machine may be characterized in terms of distributions of a performance metric associated with the entity. The method may also include implementing of the framework for characterization of the entity to manage the entity.

Abstract: Determining a sustainability impact for available data center architectures. A data center is accessed by a computer. Two or more functional parameters of the data center are then evaluated by the computer. Then, the computer combines the two or more evaluated functional parameters into a quantified judgment to determine a sustainability impact of available architectures of the data center.

Abstract: A model is provided that produces predicted sensor data as a function of at least one input feature that includes an adjustable setting of a cooling infrastructure. The model is able to model a non-linear relationship between the predicted sensor data and the adjustable setting.

Abstract: Systems and automated methods for predicting photovoltaic (PV) generation are provided. Weather forecast data and present-day and historical PV generation data are provided to respective predictors. The predictors derive weighted predictions that are used to calculate a Bayesian model average. Near-future generation by the PV system is predicted using the Bayesian model average. Production rates, worker scheduling, hours of operation and other planning decisions can be made in accordance with the predicted near-future generation.

Abstract: Systems and methods of analyzing energy consumption using node similarity are disclosed. An example of a method may be carried out by program code stored on non-transient computer-readable medium and executed by a processor. The method includes assigning a similarity score to a plurality of nodes using at least one of: domain based rules, attribute based similarity metrics, and machine learning. The method also includes identifying a similar node from the plurality of nodes, for a node under consideration, based on the similarity score. The method also includes determining energy consumption of the node under consideration based on energy consumption of the similar node.

Abstract: Multivariate time-series prediction is combined with motif discovery. Multivariate time-series prediction is performed on measured (past) data points to generate or determine predicted data points. One or more motifs are discovered or determined within the combined measured data points and the predicted data points. The motifs within the measured data points are associated with the motifs within the predicted data points to permit inferences of the motifs within the measured data points to be applied to the motifs within the predicted data points. The measured data points, the predicted data points, and the motifs are displayed. User interaction with this display of the measured data points, the predicted data points, and the motifs is permitted.