Integrating diagnostics and model-based optimization

Abstract

Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation.This paper describes the development and testing of a hybrid data-driven and physics modelbased operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system.

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

“…Owing to the time-consuming and labour-intensive characteristics of human-designed fault experiments, researchers have begun to perform FE research via fault simulations -generating fault data via fault modelling and simulations can be less expensive than the alternative. Some developed simulated fault models [90][91][92][93][94] and analysed fault impacts using software such as EnergyPlus [95][96][97], BIM [94,98,99], Modelica [100][101][102], TRNSYS [103][104][105], HVACSIM+ [106] and Simulink [107]. In addition, some practical fault-operating data can be stored or processed online for FDD and FE purposes.…”

A review of optimization approaches for controlling water-cooled central cooling systems

“…The ElectricEIR model consists of three regression equations that describe the capacity and efficiency under varying operating conditions and the power consumption under partial load conditions. Chillers' power consumption can be calculated from the reference conditions using these equations [71,94,104]. The calibration of this model requires both full-load and partial load operating data.…”

“…With a simple structure, this adaptive model can be calculated quickly, which is suitable for online applications. For cooling towers, the YorkCalc model is a simple empirical model based on varying approach temperatures, and the cooling tower energy consumption is calculated according to the affinity law [14,71,104]. The use of YorkCalc model is limited to the valid operating range and exceeding the limits may be problematic.…”

“…Gradient-free algorithms do not use any derivatives, and only the output of the objective function is iteratively compared in the optimization process. The gradient-free optimization algorithms used in this field include the exhaustive search (ExS) [18,85], Nelder-Mead simplex (NMS) [110], Hooke-Jeeves (HJ) [14,104], and branch-and-bound (B&B) algorithms [61,65]. The ExS algorithm tests possible solutions sequentially to find the optimum point, and it is normally used for discrete or discretized variables.…”

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

“…This in fact implies larger opportunities of energy savings, which in the case of public buildings, may be particularly critical, due to the use of public money.The efficient use of energy intensive equipment, such as Heating, Ventilation, and Air Conditioning (HVAC) systems, plays a key role for the development of sustainable energy buildings. It is worth noting that this applies not only to the design stage, i.e., by making use of high performing insulating materials, advanced construction technologies, efficient equipment, or renewable sources [2,3], but also to the operation stage, i.e., by defining how equipment and energy resources must be operated to ensure an efficient use of energy [4]. The efficient operation of equipment and energy resources, particularly in the case of large buildings, requires the monitoring and control of several parameters, concerning the use of facilities (i.e., the presence of users, the thermo-hygrometric conditions in rooms, etc.…”