Lianjun An

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.

Utilizing simulation to evaluate business decisions in sense-and-respond systems

Simulation can be an effective way to evaluate alternative decisions in sense-and-respond systems prior to taking actions to resolve existing or anticipated business situations. In sense-and-respond systems, business situations arise within predefined contexts that specify what aspects of the business need to be monitored and what information is needed to make decisions. We have designed a decision support system that dynamically configures simulation models based on business context and interactively presents simulation results to business analysts. In this paper, our decision support system is applied to the IBM demand conditioning process, in which mismatches between supply and demand are identified and corrective actions are initiated.

Discrete event simulation modeling of resource planning and service order execution for service businesses

In this paper, we present a framework for developing discrete-event simulation models for resource-intensive service businesses. The models simulate interactions of activities of demand planning of service engagements, supply planning of human resources, attrition of resources, termination of resources and execution of service orders to estimate business performance of resource-intensive service businesses. The models estimate serviceability, costs, revenue, profit and quality of service businesses. The models are also used in evaluating effectiveness of various resource management analytics and policies. The framework is aided by an information meta-model, which componentizes modeling objects of service businesses and allows effective integration of the components.

A system dynamics framework for sense-and-respond systems

Sense-and-respond systems realize the concepts of autonomic computing at the level of business processes. One of the key requirements to build sense-and-respond systems is to accurately capture and model the dynamical behavior of business metrics, a.k.a. key performance indicators (KPI). System dynamics (SD) models and the runtime engines provide means to understand both key performance indicators and the dynamic behaviors (e.g. causality) among them. In this paper, we present a system dynamics model based upon a scenario from supply chain management domain. Our purpose is to demonstrate an alternative approach of building sense-and-respond systems. Specifically, we use system dynamics to formally define the KPIs of both the retail inventory and the supplier backlog. Additionally, we introduce objective functions and control variables as the optimization elements being part of the system dynamics formalism. Therefore, the decision (e.g. the order size from manufacturer to suppliers) would correspond to the optimal solution of the system with respect to the defined objective. These concepts will be explained through scenarios. The enabling reference architecture and deployment method using system dynamics are also presented in this paper. After the system dynamics models and corresponding components are deployed to the field, the whole system will manifest the sense-and-respond behavior in a dynamical fashion

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.

Sense-and-respond grids for adaptive enterprises

In todays competitive climate, enterprises must strive for maximum responsiveness and adaptive capability to stay abreast of complex interactions among customers, suppliers, manufacturers, markets, and other elements of their businesses. For several years, firms have used the sense-and-respond paradigm for monitoring and managing business solutions. Called S&R for short, this method, as popularized by Stephan Haeckel, is a way to institutionalize the virtue of adaptivity (1999). A new breed of platforms and tools that use the S&R paradigm to enhance business performance management are making it possible to build an effective decision-making process into an enterprises software and equipment.

Managing workforce resource actions with multiple feedback control schemes

Demand disturbances in service businesses are typically managed by resource actions such as hiring, releasing and cross training of the workforce. The magnitudes of resource actions are often decided by estimating the discrepancy between the demand for services and the supply of workforce. However, naive feedback control of the resource actions by policies that equate the discrepancy to the control action can produce undesirable effects such as oscillation between hiring and releasing of workforce, and amplified oscillation through the stages of the service processes. Effective combination of multiple feedback control schemes can produce desirable policies of workforce resource actions. In this work, we study application of control theoretic principles in managing resource actions to see how various feedback control schemes can improve costs, utilization and stability of workforce.

Operation-oriented design of decision support systems

Operation-oriented design is a practical approach to develop a robust business solution that can reduce reengineering effort and improve reusability. Taking implementing forecast engine for business operation planning process as an example, we first discuss the requirement from operational perspective, including data sources, analytic capability. So that we can design and develop a smart and integrated supply chain, the computer implemented and Internet connected information system that aligns with its supported business process. In our example, we start with how to assess product demand from analyzing sale history and product lifecycle specification and correlating with external economic indicators. The proposed flexible IT system will enable collaboration between users in the product planning process, provides configurable and extensible platform and makes intelligent prediction of product demand incorporating market trend and product lifecycle information. Such an IT system provides not only business processs visibility but also analytic capability and intelligent decision support.

Controlling workforce resource actions for demand disturbances in services supply chains

Demand disturbances in service businesses can produce undesirable effects such as reduced service level, reduced utilization, and oscillation of workforce availability. The disturbances are typically managed by resource actions such as hiring, releasing and cross training the workforce. However, ineffective control of the resource actions can produce undesirable situations such as oscillation between hiring and releasing, and amplified oscillation through the stages of the service processes. Effective combination of multiple feedback control schemes for different demand disturbance patterns can produce desirable policies of workforce resource actions. In this work, we apply control theoretic principles in managing workforce resource actions to see how the feedback control schemes can provide decision support that leads to reduction of resource costs, improvement of resource utilization and stability of workforce availability.