Transitioning to clean and low-carbon energy is becoming a crucial goal for many entities in the energy systems sector such as governments, power utilities, and policymakers. This shift to clean energy is supported by a diverse portfolio of data products such as satellite data, smart meter data, power networks, green energy datasets (e.g., solar installations & electric vehicles), microgrid networks, and building stock data. Among these, network datasets are becoming increasingly common in addressing a wide array of issues in residential energy, especially in applications that focus on social good. Thus, streamlining the process of generating different types of networks will be helpful. In this work, we propose a versatile network synthesis and analytics pipeline developed using software design principles that make it modular, scalable, and extensible. Three case studies are presented to illustrate the significance of network data in sustainable energy applications.

Synthetic data is gaining rapid importance in many application domains due to privacy issues, bias, lack, or simply unavailability of real data. It is important that the synthetic data be a good representation of real data for successfully completing the task at hand. Thus, devising characteristic validation metrics is crucial and remains an open problem in many domains (e.g., image generation). Good validation metrics must be able to disentangle the differences between the quality and the variability coverage of the synthetic data. We propose to use a 3-dimensional metric (precision α, recall β, coverage γ) to describe the fidelity and diversity of the synthetic data. In this paper, we improve on existing definitions of precision, recall, and coverage to extend to large scale time series data. Traditional nearest neighbor manifolds from the literature are replaced by unsupervised learning techniques such as clustering to deal with large scale fine resolution time series while computing the validation metrics. The proposed metrics are employed to validate synthetic data in the domain of residential energy demand. In addition, we extend these definitions to datasets that have a natural hierarchical structure. We propose a hierarchical data-tree model in which precision, recall, and coverage can be computed at multiple inherent (and/or custom) levels of groupings of the data.

Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity.

A flaky test is a test that may fail or pass for the same code under testing (CUT). Flaky tests could be harmful to developers because the non-deterministic test outcome is not reliable and developers cannot easily debug the code. A prior study characterized the root causes and fixing strategies of flaky tests by analyzing commits of 51 Apache open source projects, without analyzing any Android app. Due to the popular usage of Android devices and the multitude of interactions of Android apps with third-party software libraries, hardware, network, and users, we were curious to find if the Android apps manifested unique flakiness patterns and called for any special resolution for flaky tests as compared to the existing literature. For this paper, we conducted an empirical study to characterize the flaky tests in Android apps. By classifying the root causes and fixing strategies of flakiness, we aimed to investigate how our proposed characterization for flakiness in Android apps varies from prior findings, and whether there are domain-specific flakiness patterns. After mining GitHub, we found 29 Android projects containing 77 commits that were relevant to flakiness. We identified five root causes of Android apps' flakiness. We revealed three novel causes -Dependency, Program Logic, and UI. Five types of resolution strategies were observed to address the flaky behavior. Many of the examined commits show developers' attempt to fix flakiness by changing software implementation in various ways. However, there are still 13% commits that simply skipped or removed the flaky tests. Our observations provide useful insights for future research on flaky tests of Android apps.

BACKGROUND: Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources - data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. RESULTS: In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. CONCLUSION: EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.