Jibonananda Sanyal

Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes

While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research.

Calibrating building energy models using supercomputer trained machine learning agents

Building energy modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters that have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making BEM unfeasible for smaller projects. In this paper, we describe the ‘Autotune’ research that employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the US building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers that are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost‐effective calibration of building models. Published 2014. This article is a US Government work and is in the public domain in the USA.

Provenance in sensor data management

A cohesive, independent solution for bringing provenance to scientific research.

Simulation and big data challenges in tuning building energy models

EnergyPlus is the flagship building energy simulation software used to model whole building energy consumption for residential and commercial establishments. A typical input to the program often has hundreds, sometimes thousands of parameters which are typically tweaked by a buildings expert to “get it right”. This process can sometimes take months. “Autotune” is an ongoing research effort employing machine learning techniques to automate the tuning of the input parameters for an EnergyPlus input description of a building. Even with automation, the computational challenge faced to run the tuning simulation ensemble is daunting and requires the use of supercomputers to make it tractable in time. In this paper, we describe the scope of the problem, particularly the technical challenges faced and overcome, and the software infrastructure developed/in development when taking the EnergyPlus engine, which was primarily designed to run on desktops, and scaling it to run on shared memory supercomputers (Nautilus) and distributed memory supercomputers (Frost and Titan). The parametric simulations produce data in the order of tens to a couple of hundred terabytes. We describe the approaches employed to streamline and reduce bottlenecks in the workflow for this data, which is subsequently being made available for the tuning effort as well as made available publicly for open-science.

Cost-effective retrofit technology for reducing peak power demand in small and medium commercial buildings

This article describes a cost-effective retrofit technology that uses collective control of multiple rooftop air-conditioning units to reduce the peak power consumption of small and medium commercial buildings. The proposed control uses a model of the building and air-conditioning units to select an operating schedule for the air-conditioning units that maintains a temperature set-point subject to a constraint on the number of units that may operate simultaneously. A prototype of this new control system was built and deployed in a large gymnasium to coordinate four rooftop air-conditioning units. Based on data collected while operating this prototype, it is estimated that the cost savings achieved by reducing peak power consumption is sufficient to repay the cost of the prototype within a year.

In-Depth Analysis of Simulation Engine Codes for Comparison with DOE s Roof Savings Calculator and Measured Data

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Mapping near-real-time power outages from social media

ABSTRACT Social media, including Twitter, has become an important source for disaster response. Yet most studies focus on a very limited amount of geotagged data (approximately 1% of all tweets) while discarding a rich body of data that contains location expressions in text. Location information is crucial to understanding the impact of disasters, including where damage has occurred and where the people who need help are situated. In this paper, we propose a novel two-stage machine learning- and deep learning-based framework for power outage detection from Twitter. First, we apply a probabilistic classification model using bag-of-ngrams features to find true power outage tweets. Second, we implement a new deep learning method–bidirectional long short-term memory networks–to extract outage locations from text. Results show a promising classification accuracy (86%) in identifying true power outage tweets, and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets. The method of identifying location names used in this paper does not require language- or domain-specific external resources such as gazetteers or handcrafted features, so it can be extended to other situational awareness analyzes and new applications.