Raya Horesh

Modeling and simulation of building energy performance for portfolios of public buildings

In the U.S., commercial and residential buildings and their occupants consume more than 40% of total energy and are responsible for 45% of total greenhouse gas (GHG) emissions. Therefore, saving energy and costs, improving energy efficiency and reducing GHG emissions are key initiatives in many cities and municipalities and for building owners and operators. To reduce energy consumption in buildings, one needs to understand patterns of energy usage and heat transfer as well as characteristics of building structures, operations and occupant behaviors that influence energy consumption. We develop heat transfer inverse models and statistical models that describe how energy is consumed in commercial buildings, and simulate the impact of energy saving changes that can be made to commercial buildings including structural, operational, behavioral and weather changes, on energy consumption and GHG emissions. The analytic toolset identifies energy savings opportunities and quantifies the savings for a large portfolio of public buildings.

Applying science and mathematics to big data for smarter buildings

Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios.

An inverse PDE-ODE model for studying building energy demand

Development of an accurate heat transfer model of buildings is of high importance. Such a model can be used for analyzing energy efficiency of buildings, predicting energy consumption and providing decision support for energy efficient operation of buildings. In this paper, we propose a PDE-ODE hybrid model to describe heat transfer through building envelope as well as heat evolution inside building. A inversion procedure is presented to recover parameters of equations from sensor data and building characteristic so that the model represents a specific building with current physical condition. By matching the simulated temperature and thermal energy dynamic profile with EnergyPlus generated data and actual field data, we validate the model and demonstrate its capability to predict energy demand under various operation condition.

Information retrieval, fusion, completion, and clustering for employee expertise estimation

Estimating the skills, talents, and expertise of employees is essential for human capital management in knowledge-based organizations across industries and sectors. In this paper, we describe an approach to infer the expertise of employees from their enterprise data and digital footprints. Using a novel big data workflow with components of information retrieval and search, data fusion, matrix completion, and ordinal regression clustering, we are able to automatically find evidence of expertise and determine appropriate evidence weights for different queries and data sources that we merge and present in a manner consumable by businesspeople. We illustrate the system on sample data from the IBM Corporation where it has been deployed.

Simulation and optimization of energy efficient operation of HVAC system as demand response with distributed energy resources

Optimal control of buildings HVAC (Heating Ventilation and Air Conditioning) system as a demand response may not only reduce energy cost in buildings, but also reduce energy production in grid, stabilize energy grid and promote smart grid. In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC system as demand response. A Nonlinear Autoregressive Neural Network (NARNET) is used to model the thermal behavior of the building zone and to simulate various HVAC control strategies. The optimal control problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem and it is used to compute the optimal control profile that minimizes the total energy costs of powering HVAC system considering dynamic demand response signal, on-site energy storage system and energy generation system while satisfying thermal comfort of building occupants within the physical limitation of HVAC equipment, on-site energy storage and generation systems.