- The aim of the Project
- State of the Art Technology
- Technological developments
- Expected results
Commercial building owners spend 30% of their operating budget on energy. Costs can be reduced with improved building energy management practices. Optimizing building performance also reduces demand for energy from the grid, lowers carbon emissions from electricity generation and fuels burned on site, and can improve occupant comfort.
Today, buildings can fall anywhere along the continuum of data management and analytics: trending, benchmarking, fault detection and diagnostics, forecasting, customized software applications, cloud computing data analysis.
Fault Detection and Diagnostics (FDD) analysis enables ongoing monitoring-based commissioning of building systems to save energy and extend equipment life. Faults relate to a system’s performance, meaning the system is operating but is performing sub-optimally. FDD tools acquire electricity, gas, steam, temperature, humidity, or chilled water energy consumption data, typically at the building level via an energy management platform, and compare those values to expected baseline energy consumption levels. The tool flags building energy consumption as high when it exceeds the baseline value by a predetermined threshold (to avoid false alarms). Often, the baseline takes into account one or more key explanatory variables, such as outdoor air temperature, building and equipment schedules, time of day or year, temperature set points, and plant configurations. Table 1 describes the broad range of possible baselines for FDD in order of increasing complexity. FDD is an important type of analysis to complete because inefficient equipment operation attributed to inadequate initial commissioning, operational issues, and real time performance degradation can waste an estimated 15 to 30% of energy used in commercial buildings. However, current FDD methods are limited to classification and pattern recognition to detect and diagnose faults.
The main goal of this project is to develop expert intelligence tools to be incorporated in commercial energy management platforms for proactive energy management in buildings to further optimize energy savings. To this end, three (3) fuzzy logic powered tools will be deployed to be incorporated in well-known SaaS platforms used for energy management to further improve decision making related to smart fault detection and diagnostics as well as intelligent predictive maintenance of basic equipment as follows:
- an expert FDD analyser,
- an HVAC system optimizer, and
- a maintenance expert.
Proactive fault diagnostics is the process of proactively manipulating system inputs to perform a diagnostic test. It consists of a diagnostic part and a proactive testing part. The proactive testing part manipulates/directs the input and the diagnostic part analyses the output to assess the system’s health status. The goal of proactive testing is to control adjustable parameters—for example, set-point, mode, schedule, damper position, fan speed, and so on—to facilitate and complete the diagnostic process. Fuzzy logic has turned out to be a popular choice for these types of problems. The inherent flexibility embedded in fuzzy sets and fuzzy rules make it a suitable solution for reasoning in domains affected by uncertainty and error. In building HVAC systems, fuzzy-based diagnostic mechanisms have been used in several studies. In this project, during the implementation of our expert FDD analyser we will develop fuzzy-based diagnostic routines for fault detection and diagnosis of various units of common building equipment, such as HVACs, PV plants, pumps, etc. In our approach, fuzzy-based inference mechanisms will compare system outputs with the predictions of simplified models at various operating points to draw conclusions about the system health status.
On the other hand, next generation BEMSs will help building owners to better manage the performance of their buildings, anticipate potential for savings or HVAC issues before they happen, and shift operations and maintenance team efforts from reactive to proactive systems management. Besides, solution providers have a specific interest in meeting the needs of smaller facilities. Vendors from the traditional automation and control segment, IT and niche specialty providers have identified HVAC system optimization as a specific avenue through which BEMSs can support smaller facilities. For instance, to decrease the energy consumption researchers in the area of thermal comfort have learned that the required indoor temperature of a building is not a fixed value. In fact, the certain range of temperatures is sufficient to create a comfortable situation. Therefore, the ideal HVAC system works with high efficiency supplying only the amount of heat, cold and air that is necessary to maintain internal conditions at a level providing thermal comfort to room occupants. In this project, users can benefit from more savings with the advanced features of our HVAC system optimizer - based on a fuzzy inference engine -, such as operating set point adjustment, inference control for variable frequency drives, etc.
Finally, when building system components unexpectedly fail, you face costly downtime and unsatisfiedoccupants. With predictive maintenance, you’ll identify potential hidden problems prior to breakdowns and proactively schedule repairs at times that won’t inconvenience you or your building’s occupants. Furthermore, predictive maintenance helps you control energy costs, prolong equipment life and prevent overtime costs for unscheduled repairs. With an expert system the status and past maintenance history of basic building equipment are used to do the work in a thorough and consistent manner. An expert system for predictive maintenance requires several functions to make it usable to a building owner or professional, including configuration ability, site visit types, user interaction, explanations, graphics, context sensitivity, displaying readings, logging conditions, deferring work, reports, comments and display. Whether this expert system is successful or not depends on an additional important factor: to take into account the availability of a real expert in the discipline, familiar with all operations and problems with the building systems services. In this project, our maintenance expert will be a fuzzy logic expert tool to predict future maintenance needs of basic building equipment by leveraging past performance, logging conditions, reports, prior FDD analysis, ESCOs knowledge and support, etc.
The successful implementation of this project will produce a number of useful and innovative results.
Three (3) fuzzy logic powered tools will be deployed to be incorporated in well-known SaaS platforms used for energy management to further improve decision making related to smart fault detection and diagnostics as well as intelligent predictive maintenance of basic equipments.
The first tool, the so-called expertFDD analyser, will be a core fault detection and diagnostics tool related to common building equipment, such as HVACs, PV plants, pumps, etc., enhancing ongoing monitoring-based commissioning of building systems to save energy and extend equipment life.
The second tool, the so-called HVAC system optimizer, will be based on a fuzzy inference engine for optimizing HVAC system operation in order to increase comfort, reduce energy costs and shift peak demand.
The third tool, the so-called maintenance expert, will be a cost-effective maintenance tool to predict future maintenance needs on basic building equipment with the view of further optimizing its operation across time.
In addition some excellent theoretical results are expected. New control mechanisms using fuzzy logic will be developed:
- fuzzy-based diagnostic routines for fault detection and diagnosis of various units of common building equipment, such as HVACs, PV plants, pumps, etc.,
- a fuzzy inference engine for optimizing HVAC system operation, and
- a fuzzy expert system to predict future maintenance needs of basic building equipment
In this perspective, the superiority of fuzzy logic controllers (FLCs) will be proved and properly disseminated. Some other additional outcomes are as follows:
- development of smart building energy management technologies
- implementation of technological advances on FDD of basic building equipment
- deployment of fuzzy techniques for disaggregation of cloud based energy loads into high level consumption categories
- adoption of new trends in HVAC systems’ optimisation
- application of expert reasoning to maintenance issues of basic building equipment
- know-how transfer between the participating companies