Results for “Graphical model selection” — Page 3

DOE OSTI2022

Learning Planar Ising Models Software

Learning Planar Ising Models is a software package written in Matlab for learning relationships among variable in a dataset using graphical models. The software package implements a generally-applicable algorithm for learning planar Ising models from any multivariate dataset. The code provides an algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. The software includes demonstrations of the algorithm in simulations and for applications on publicly available datasets. Details of the algorithm, demonstration simulations, and applications are given in Johnson, et al; 2016. Reference: Johnson, J. K., Oyen, D., Chertkov, M., and Netrapalli, P. (2016). Learning planar Ising models. Journal of Machine Learning Research.

Oyen, Diane↗

DOE OSTI2019

Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations

The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

CDL2PLC translator v0.1.0

The CDL-PLC translator aims at translating control sequences for building energy systems from the CDL CXF format to the PLCopen XML format. The CDL CXF developed at LBL within the OpenBuildingControl project, and now being standardized via ASHRAE Standard 231P, enables expressing control sequences developed in the simulation environment Modelica in a JSON format. The PLCopen XML is an existing exchange format standardized in IEC 61131-10 for Programmable Logic Controllers (PLCs) following the IEC 61131 standard as one target system of CDL among others. The translation from the CDL CXF to the PLCopen XML contributes to a seamless workflow from the model-based development of control sequences in simulation environments, which is not building practice today, and their digital implementation on building controllers, which replaces graphical and textual documents used for this purpose today. The translator is at a prototypical stage and enables, as a proof of concept, the translation of very simple control sequences composed of 4 selected function blocks out of 137 function blocks defined in CDL. The translation includes the connection of inputs and outputs of function blocks and the expression of a control function in CDL to the equivalent code in IEC 61131-3.

Walther, Karl↗

DOE OSTI2014

Web-based, GPU-accelerated, Monte Carlo simulation and visualization of indirect radiation imaging detector performance

Purpose: Monte Carlo simulations play a vital role in the understanding of the fundamental limitations, design, and optimization of existing and emerging medical imaging systems. Efforts in this area have resulted in the development of a wide variety of open-source software packages. One such package, hybridMANTIS, uses a novel hybrid concept to model indirect scintillator detectors by balancing the computational load using dual CPU and graphics processing unit (GPU) processors, obtaining computational efficiency with reasonable accuracy. In this work, the authors describe two open-source visualization interfaces, webMANTIS and visualMANTIS to facilitate the setup of computational experiments via hybridMANTIS. Methods: The visualization tools visualMANTIS and webMANTIS enable the user to control simulation properties through a user interface. In the case of webMANTIS, control via a web browser allows access through mobile devices such as smartphones or tablets. webMANTIS acts as a server back-end and communicates with an NVIDIA GPU computing cluster that can support multiuser environments where users can execute different experiments in parallel. Results: The output consists of point response and pulse-height spectrum, and optical transport statistics generated by hybridMANTIS. The users can download the output images and statistics through a zip file for future reference. In addition, webMANTIS provides a visualization window that displays a few selected optical photon path as they get transported through the detector columns and allows the user to trace the history of the optical photons. Conclusions: The visualization tools visualMANTIS and webMANTIS provide features such as on the fly generation of pulse-height spectra and response functions for microcolumnar x-ray imagers while allowing users to save simulation parameters and results from prior experiments. The graphical interfaces simplify the simulation setup and allow the user to go directly from specifying input parameters to receiving visual feedback for the model predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

TRACC: An open source software for processing sap flux data from thermal dissipation probes

Here, thermal dissipation probes (TDPs) have become a widely used method of monitoring plant water use in recent years. The use of TDPs requires calibration to a theoretical zero-flow value (ΔT0); usually based upon the assumption that at least some nighttime measurements represent zero-flow conditions. Fully automating the processing of data from TDPs is made exceedingly difficult due to errors arising from many sources. However, it is desirable to minimize variation arising from different researchers’ processing data, and thus, a common platform for processing data, including editing raw data and determination of ΔT0, is useful and increases the transparency and replicability of TDP-based research. Here, we present the TDP data processing software TRACC (Thermal dissipation Review Assessment Cleaning and Conversion) to serve this purpose. TRACC is an open-source software written in the language R, using graphical presentation of data and on screen prompts with yes/no or simple numerical responses. It allows the user to select several important options, such as calibration coefficients and the exclusion of nights when vapor pressure deficit does not approach zero. Although it is designed for users with no coding experience, the outputs of TRACC could be easily incorporated into more complex models or software.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).