Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model

Considering the ever-growing increase in the world energy consumption and the fact that buildings contribute a large portion of the global energy consumption arises a need for detailed investigation towards more effective energy performance of buildings. Thus, monitoring, estimating, and reducing buildings’ energy consumption have always been important concerns for researchers and practitioners in the field of energy management. Since more than 80% of energy consumption happens during the operation phase of a building’s life cycle, efficient management of building operation is a promising way to reduce energy usage in buildings. Among the parameters influencing the total building energy consumption, building occupants’ presence and preferences could have high impacts on the energy usage of a building. To consider the effect of occupancy on building energy performance, different occupancy models, which aim to estimate the space utilization patterns, have been developed by researches. However, providing a comprehensive occupancy model, which could capture all important occupancy features, is still under development. Moreover, researchers investigated the effect of the application of occupancy-centered control strategies on the efficiency of the energy-consuming systems. However, there are still many challenges in this area of research mainly related to collecting, processing, and analyzing the occupancy data and the application of intelligent control strategies. In addition, generally, there is an inverse relationship between the energy consumption of operational systems and the comfort level of occupants using these systems. As a result, finding a balance between these two important concepts is crucial to improve the building operation. The optimal operation of building energy-consuming systems is a complex procedure for decision-makers, especially in terms of minimizing the energy cost and the occupants’ discomfort. On this premise, this research aims to develop a new simulation-based multi-objective optimization model of the energy consumption in open-plan offices based on occupancy dynamic profiles and occupants’ preferences and has the following objectives: (1) developing a method for extracting detailed occupancy information with varying time-steps from collected Real-Time Locating System (RTLS) occupancy data. This method captures different resolution levels required for the application of intelligent, occupancy-centered local control strategies of different building systems; (2) developing a new time-dependent inhomogeneous Markov chain occupancy prediction model based on the derived occupancy information, which distinguishes the temporal behavior of different occupants within an open-plan office; (3) improving the performance of the developed occupancy prediction model by determining the near-optimum length of the data collection period, selecting the near-optimum training dataset, and finding the most satisfying temporal resolution level for analyzing the occupancy data; (4) developing local control algorithms for building energy-consuming systems; and (5) integrating the energy simulation model of an open-plan office with an optimization algorithm to optimally control the building energy-consuming systems and to analyze the trade-off between building energy consumption and occupants’ comfort. It is found that the occupancy perdition model is able to estimate occupancy patterns of the open-plan office with 92% and 86% accuracy at occupant and zone levels, respectively. Also, the proposed integrated model improves the thermal condition by 50% along with 2% savings in energy consumption by developing intelligent, optimal, and occupancy-centered local control strategies

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97 M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8,$2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. It showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset

Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure