The aim of this research is to assess the potential impact of the CO2 Performance Ladder on CO2 emission reduction. The CO2 Performance Ladder is a new green procurement scheme that has been adopted by several public authorities in the Netherlands; it is a staged certification scheme for energy and CO2 management. The achieved certification level gives companies a certain competitive advantage in contract awarding procedures. While the scheme has been widely adopted by companies in the construction industry, other types of companies in the supply chain of the commissioning parties also participate. Currently, more than 190 companies participate in the scheme. The aggregate CO2 emissions covered by the scheme are around 1.7 Mtonnes, which corresponds to almost 1 % of national greenhouse gas emissions in the Netherlands. Since the introduction of the scheme the total CO2 emissions have decreased substantially. Nevertheless, these emission reductions should be interpreted with caution since emission reductions are dominated by a few companies and are affected to a large extent by economic activity. Companies participating in the scheme have set different types of CO2 emission reduction targets with varying ambition levels. The projected impact of these targets on CO2 emissions is in the range of a 0.5 %-1.3 % absolute emission reduction per year, with a most likely value of 1.1 %. The CO2 Performance Ladder can therefore make a substantial contribution to achieving the CO2 emission reductions for non-ETS sectors in the Netherlands up to 2020.
The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
This report is an introduction in into the topic of sustainability and the world of sports. It contains literature studies and several empirical studies. It provides overviews of current academic and professional sources and suggests further research.
