Elie Azar

Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings

AbstractEnergy modeling is globally used during the design phase to estimate future building energy performance. Predictions obtained from common energy estimation software typically deviate from actual energy consumption levels. This discrepancy can mainly be attributed to the misrepresentation of the role that building occupants play in the energy estimation equation. Although occupants might have different and varying energy use characteristics over time, current energy estimation tools assume they are constant. This paper proposes a new agent-based approach to commercial building energy modeling by accounting for the diverse and dynamic energy consumption patterns among occupants, in addition to the potential changes in their energy use behavior attributable to their interactions with the building environment and with each other. The impact of an active modeling of occupancy is then illustrated in a case study of an office in a university building, where more than 25% variation in the predicted energy...

A conceptual framework to energy estimation in buildings using agent based modeling

Actual energy consumption in buildings is typically different from predictions during the design phase. While differences in occupant energy usage characteristics play an important role in this variation, actual energy estimation software do not account for this factor. This paper proposes a new approach for energy estimation in buildings using a combination of traditional energy calculation software along with agent-based simulation modeling. First, the difference in energy consumption levels for different types of occupancy energy usage characteristics is identified by building energy models adapted for each type of behavior. Then, an agent-based simulation model simulates the influence that people with different behaviors have on each other, resulting in potential changes in energy usage characteristics over time. By combining these two methods, more customized energy studies can be performed resulting in more accurate energy consumption estimates.

Recession effects in United States public sector construction contracting: Focus on the American Recovery and Reinvestment Act of 2009

AbstractThe 2009 American Recovery and Reinvestment Act (ARRA) was enacted to support the economy in response to the 2008–2009 global recession. The injection of construction funds into the public sector put pressure on public agencies to award contracts as quickly as possible in an effort to immediately stimulate the economy and help reconstruct the deteriorating infrastructure. This enabled contractors to move from private sector work into the public sector to stay afloat financially. As a result, an increase in competition for public project awards was observed combined with projects being awarded at a fast rate. The main objective of this paper is to investigate evidence of the level of contractor competition and the degree to which public agencies are expediting the award process during the bidding phase on ARRA construction projects. Both positive and negative impacts caused by this dynamic are examined. To achieve this objective, the research looked into the bidding phase of ARRA construction proje...

Sensitivity of Energy Simulation Models to Occupancy Related Parameters in Commercial Buildings

Commercial buildings are responsible for 19 percent of the total energy consumption in the United States (US) and are projected to expand their share of energy use at increasing rates. Saving energy in the commercial building sector has therefore become the focus of many governmental initiatives. The first step towards more energy efficient buildings is improving design. Optimizing the sizing of mechanical and electrical systems is particularly important as it highly affects the buildings’ life-cycle energy use. Consequently, designers and engineers are using energy modeling and simulation to compare different systems and predict building performance. Large discrepancies are however being observed between predicted and actual building performances. In order to improve these predictions, the sensitivity of energy models to different input parameters needs to be evaluated. Although proven to significantly affect energy use, occupancy related parameters have rarely been evaluated. This paper presents a sensitivity analysis study performed on the occupancy parameters of the most typical commercial building type encountered in the United States (US). Results indicate that occupancy chosen building temperature set points have the highest impact on energy use, while the influence of building schedule and after-hours equipment/lighting use is also significant, particularly in hot weather climates.

Multilayer Agent-Based Modeling and Social Network Framework to Evaluate Energy Feedback Methods for Groups of Buildings

AbstractImportant energy reductions can be achieved in the building sector by providing occupants with feedback about their energy-consumption levels. Recent studies link the success of energy-feed...

Evaluation of tree-based ensemble learning algorithms for building energy performance estimation

Tree-based ensemble learning has received significant interest as one of the most reliable and broadly applicable classes of machine learning techniques. However, thus far, it has rarely been used to model and evaluate the drivers of energy consumption in buildings and as such its performance and accuracy in this field have yet to be properly tested or fully understood. The goal of this paper is to evaluate the performance of three ensemble learning algorithms in modelling and predicting the heating and cooling loads of buildings, namely (i) random forests, (ii) extremely randomized trees (extra-trees), and (iii) gradient boosted regression trees. Results show that the tested algorithms outperform the ones proposed in the recent literature, with gradient boosting improving on the prediction accuracy of the second best-performing algorithm by an average of 14% and 65% for the heating and cooling loads, respectively.

A data collection and analysis framework to improve the performance of energy-intensive commercial buildings

Commercial buildings show large discrepancies between estimated and actual energy consumption levels. Important challenges specifically lay in understanding the influence of human actions (by occupants and facility managers) on energy use and their contribution to commonly observed high energy consumption levels. This is particularly relevant in energy-intensive buildings such as laboratory facilities, which can typically consume up to five times more energy than other types of commercial buildings. This paper presents a framework to (1) collect relevant data about building energy performance and occupancy, (2) mine data, and (3) adapt and use energy analysis methods to understand the influence of occupants on energy performance, detect inefficiencies in the operation of building systems, and propose solutions. The framework is illustrated though a case study on a laboratory building in Madison, WI, identifying an excess of 40 percent in the energy consumption of the Heating, Ventilation and Air Conditioning System (HVAC).

Influence of social sub-networks on energy conservation from occupancy interventions in a typical U.S. commercial building

Occupancy focused interventions used to reduce building energy use (e.g., feedback) are exhibiting low and un-sustained energy reductions. Research on residential buildings has shown that the type of building social network determines the effectiveness of these interventions. However, the findings cannot be directly applied to commercial buildings given their more complex social structures formed by independent entities (i.e., companies) with different organizational structures and cultures. Therefore, the objective of this study is to evaluate the influence of social sub-networks on the energy conservation from occupancy focused interventions. An agent-based model was developed for this purpose simulating sub-networks in a typical commercial building environment. Occupancy interventions were then tested while varying the number of sub-networks in the building. Statistical analyses were finally performed showing that modeling sub-networks significantly affects the effectiveness of occupancy interventions and need to be accounted for in the design of more efficient interventions for commercial buildings.

Quantifying the impact of uncertainty in human actions on the energy performance of educational buildings

Actions taken by building occupants and facility managers can have significant impacts on building energy performance. Despite the growing interesting in understanding human drivers of energy consumption, literature on the topic remains limited and is mostly focused on studying individual occupancy actions (e.g., changing thermostat set point temperatures). Consequently, the impact of uncertainty in human actions on overall building performance remains unclear. This paper proposes a novel method to quantify the impact of potential uncertainty in various operation actions on building performance, using a combination of Monte Carlo and Fractional Factorial analyses. The framework is illustrated in a case study on educational buildings, where deviations from base case energy intensity levels exceed 50 kWh/m2/year in some cases. The main contributors to this variation are the thermostat temperature set point settings, followed by the consumption patterns of equipment and lighting systems by occupants during unoccupied periods.

Coupling Distributed Energy Simulation and Occupancy Models for Comprehensive Building Energy Consumption Analysis

We propose a conceptual framework that couples energy modeling with occupancy characteristics and energy use data to achieve comprehensive building energy consumption analysis. Specifically, we aim: 1) to couple distinct and spatially distributed simulation models and synchronize their data exchange; and 2) to demonstrate the coupled simulation through a hypothetical case example of a building. This framework has been developed based on the principles defined in the High-Level Architecture (HLA) that enables distributed computing. In other words, the HLA-compliant federation allows federates (e.g., models) to communicate with each other and exchange relevant information to achieve the global objective of analyzing and reducing the building‟s energy use. A case study of a typical commercial building illustrates how the federates coordinate data synchronization and run in a distributed fashion. This example tests feedback frequency to building occupants on the building‟s energy use and illustrates the potential application of the framework to study energy interventions in buildings.