RESEARCH
UCSD Digital Twin
A digital twin is set of adaptive models that emulate the behaviour of a physical system in a virtual system getting real time data to update itself along its life cycle. The digital twin replicates the physical system to predict failures and opportunities for changing, to prescribe real time actions for optimizing and/or mitigating unexpected events observing and evaluating the operating profile system.
Netload
The U.S. Department of Energy (DOE) has one of the richest and most diverse histories in the federal government. Although only in existence since 1977, the Department traces its lineage to the Manhattan Project effort to develop the atomic bomb during World War II and to the various energy-related programs that previously had been dispersed throughout various federal agencies.
Walmart - Baselining, Design and Modeling
The Baseline Period Measurement and Verification Plan, prepared by the Center for Sustainable Energy, defines the baseline energy and water performance of the site including which building and system components will be monitored, the monitoring interval and period, the equipment necessary to obtain the required data, and other details related to equipment installation planning.
The project team will design and install a holistic suite of precommercial energy efficiency (EE) technologies for the Covina Walmart building. P2S will design the energy efficiency technology package and prepare a basis of design, design documents and construction documents.
To inform the energy efficiency technology package selected, NREL used the Department of Energy’s OpenStudio Modeling Software to complete baseline and calibrated energy models. They also performed an optimization study whereby the baseline energy model is run with various energy efficiency technology packages.
Each energy efficiency package will include different combinations of precommercial technologies to determine the best energy efficiency package, as well as the best scheduling of technologies when interacting with one another. Additionally, the final calibrated energy model will be utilized for comparison during a set measurement and verification period after the installation of all selected technologies.
Energy prediction
This is machine learning-based energy prediction algorithm aimed at forecasting power consumption using historical data and weather conditions. The goal is to enhance automation and optimize battery discharge in buildings to reduce peak power costs. Several models were tested, including Linear Regression, Random Forest, XGBoost, and Neural Networks, with results varying across different business cases (e.g., Zion, Stellar Care, and Sweet Water Gas). The study highlights challenges such as seasonal variations and occupancy effects, with XGBoost and regression models showing the best performance.
OpenADR
OpenADR is a standard for automating demand response in energy management, helping utilities and customers optimize electricity use.
Load Shedding Capacity
Developed a machine learning-based energy prediction system to forecast daily power consumption using historical usage data and weather features, enabling automated demand response and cost optimization for businesses.
3D Printing
Developed a machine learning-based energy prediction system to forecast daily power consumption using historical usage data and weather features, enabling automated demand response and cost optimization for businesses.
GitHub
GitHub is a web-based service for hosting software projects and their collaborative development on a remote Internet server in a so-called repository. It is based on the Git version control and management systems.
