Building effectiveness communication ratios (BECs): an integrated ‘life-cycle’ methodology for mitigating energy-use in buildings

Current building regulations are generally prescriptive in nature. It is widely accepted in Europe that this form of building regulation is stifling technological innovation and leading to inadequate energy efficiency in the building stock. This has increased the motivation to move design practices towards a more ‘performance-based’ model in order to mitigate inflated levels of energy-use consumed by the building stock. A performance based model assesses the interaction of all building elements and the resulting impact on holistic building energy-use. However, this is a nebulous task due to building energy-use being affected by a myriad of heterogeneous agents. Accordingly, it is imperative that appropriate methods, tools and technologies are employed for energy prediction, measurement and evaluation throughout the project’s life cycle. This research also considers that it is imperative that the data is universally accessible by all stakeholders. The use of a centrally based product model for exchange of building information is explored. This research describes the development and implementation of a new building energy-use performance assessment methodology. Termed the Building Effectiveness Communications ratios (BECs) methodology, this performance-based framework is capable of translating complex definitions of sustainability for energy efficiency and depicting universally understandable views at all stage of the Building Life Cycle (BLC) to the project’s stakeholders. The enabling yardsticks of building energy-use performance, termed Ir and Pr, provide continuous design and operations feedback in order to aid the building’s decision makers. Utilised effectively, the methodology is capable of delivering quality assurance throughout the BLC by providing project teams with quantitative measurement of energy efficiency. Armed with these superior enabling tools for project stakeholder communication, it is envisaged that project teams will be better placed to augment a knowledge base and generate more efficient additions to the building stock.

Bottom-up modelling of energy demand and technical energy savings potential in the Irish residential sector

The International Energy Agency has repeatedly identified increased end-use energy efficiency as the quickest, least costly method of green house gas mitigation, most recently in the 2012 World Energy Outlook, and urges all governing bodies to increase efforts to promote energy efficiency policies and technologies. The residential sector is recognised as a major potential source of cost effective energy efficiency gains. Within the EU this relative importance can be seen from a review of the National Energy Efficiency Action Plans (NEEAP) submitted by member states, which in all cases place a large emphasis on the residential sector. This is particularly true for Ireland whose residential sector has historically had higher energy consumption and CO2 emissions than the EU average and whose first NEEAP targeted 44% of the energy savings to be achieved in 2020 from this sector. This thesis develops a bottom-up engineering archetype modelling approach to analyse the Irish residential sector and to estimate the technical energy savings potential of a number of policy measures. First, a model of space and water heating energy demand for new dwellings is built and used to estimate the technical energy savings potential due to the introduction of the 2008 and 2010 changes to part L of the building regulations governing energy efficiency in new dwellings. Next, the author makes use of a valuable new dataset of Building Energy Rating (BER) survey results to first characterise the highly heterogeneous stock of existing dwellings, and then to estimate the technical energy savings potential of an ambitious national retrofit programme targeting up to 1 million residential dwellings. This thesis also presents work carried out by the author as part of a collaboration to produce a bottom-up, multi-sector LEAP model for Ireland. Overall this work highlights the challenges faced in successfully implementing both sets of policy measures. It points to the wide potential range of final savings possible from particular policy measures and the resulting high degree of uncertainty as to whether particular targets will be met and identifies the key factors on which the success of these policies will depend. It makes recommendations on further modelling work and on the improvements necessary in the data available to researchers and policy makers alike in order to develop increasingly sophisticated residential energy demand models and better inform policy.

Climate and energy scenarios for Ireland to 2050 using the Irish TIMES energy systems model

Due to growing concerns regarding the anthropogenic interference with the climate system, countries across the world are being challenged to develop effective strategies to mitigate climate change by reducing or preventing greenhouse gas (GHG) emissions. The European Union (EU) is committed to contribute to this challenge by setting a number of climate and energy targets for the years 2020, 2030 and 2050 and then agreeing effort sharing amongst Member States. This thesis focus on one Member State, Ireland, which faces specific challenges and is not on track to meet the targets agreed to date. Before this work commenced, there were no projections of energy demand or supply for Ireland beyond 2020. This thesis uses techno-economic energy modelling instruments to address this knowledge gap. It builds and compares robust, comprehensive policy scenarios, providing a means of assessing the implications of different future energy and emissions pathways for the Irish economy, Ireland’s energy mix and the environment. A central focus of this thesis is to explore the dynamics of the energy system moving towards a low carbon economy. This thesis develops an energy systems model (the Irish TIMES model) to assess the implications of a range of energy and climate policy targets and target years. The thesis also compares the results generated from the least cost scenarios with official projections and target pathways and provides useful metrics and indications to identify key drivers and to support both policy makers and stakeholder in identifying cost optimal strategies. The thesis also extends the functionality of energy system modelling by developing and applying new methodologies to provide additional insights with a focus on particular issues that emerge from the scenario analysis carried out. Firstly, the thesis develops a methodology for soft-linking an energy systems model (Irish TIMES) with a power systems model (PLEXOS) to improve the interpretation of the electricity sector results in the energy system model. The soft-linking enables higher temporal resolution and improved characterisation of power plants and power system operation Secondly, the thesis develops a methodology for the integration of agriculture and energy systems modelling to enable coherent economy wide climate mitigation scenario analysis. This provides a very useful starting point for considering the trade-offs between the energy system and agriculture in the context of a low carbon economy and for enabling analysis of land-use competition. Three specific time scale perspectives are examined in this thesis (2020, 2030, 2050), aligning with key policy target time horizons. The results indicate that Ireland’s short term mandatory emissions reduction target will not be achieved without a significant reassessment of renewable energy policy and that the current dominant policy focus on wind-generated electricity is misplaced. In the medium to long term, the results suggest that energy efficiency is the first cost effective measure to deliver emissions reduction; biomass and biofuels are likely to be the most significant fuel source for Ireland in the context of a low carbon future prompting the need for a detailed assessment of possible implications for sustainability and competition with the agri-food sectors; significant changes are required in infrastructure to deliver deep emissions reductions (to enable the electrification of heat and transport, to accommodate carbon capture and storage facilities (CCS) and for biofuels); competition between energy and agriculture for land-use will become a key issue. The purpose of this thesis is to increase the evidence-based underpinning energy and climate policy decisions in Ireland. The methodology is replicable in other Member States.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.

Systematic delay-driven power optimisation and power-driven delay optimisation of combinational circuits

With the proliferation of mobile wireless communication and embedded systems, the energy efficiency becomes a major design constraint. The dissipated energy is often referred as the product of power dissipation and the input-output delay. Most of electronic design automation techniques focus on optimising only one of these parameters either power or delay. Industry standard design flows integrate systematic methods of optimising either area or timing while for power consumption optimisation one often employs heuristics which are characteristic to a specific design. In this work we answer three questions in our quest to provide a systematic approach to joint power and delay Optimisation. The first question of our research is: How to build a design flow which incorporates academic and industry standard design flows for power optimisation? To address this question, we use a reference design flow provided by Synopsys and integrate in this flow academic tools and methodologies. The proposed design flow is used as a platform for analysing some novel algorithms and methodologies for optimisation in the context of digital circuits. The second question we answer is: Is possible to apply a systematic approach for power optimisation in the context of combinational digital circuits? The starting point is a selection of a suitable data structure which can easily incorporate information about delay, power, area and which then allows optimisation algorithms to be applied. In particular we address the implications of a systematic power optimisation methodologies and the potential degradation of other (often conflicting) parameters such as area or the delay of implementation. Finally, the third question which this thesis attempts to answer is: Is there a systematic approach for multi-objective optimisation of delay and power? A delay-driven power and power-driven delay optimisation is proposed in order to have balanced delay and power values. This implies that each power optimisation step is not only constrained by the decrease in power but also the increase in delay. Similarly, each delay optimisation step is not only governed with the decrease in delay but also the increase in power. The goal is to obtain multi-objective optimisation of digital circuits where the two conflicting objectives are power and delay. The logic synthesis and optimisation methodology is based on AND-Inverter Graphs (AIGs) which represent the functionality of the circuit. The switching activities and arrival times of circuit nodes are annotated onto an AND-Inverter Graph under the zero and a non-zero-delay model. We introduce then several reordering rules which are applied on the AIG nodes to minimise switching power or longest path delay of the circuit at the pre-technology mapping level. The academic Electronic Design Automation (EDA) tool ABC is used for the manipulation of AND-Inverter Graphs. We have implemented various combinatorial optimisation algorithms often used in Electronic Design Automation such as Simulated Annealing and Uniform Cost Search Algorithm. Simulated Annealing (SMA) is a probabilistic meta heuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. We used SMA to probabilistically decide between moving from one optimised solution to another such that the dynamic power is optimised under given delay constraints and the delay is optimised under given power constraints. A good approximation to the global optimum solution of energy constraint is obtained. Uniform Cost Search (UCS) is a tree search algorithm used for traversing or searching a weighted tree, tree structure, or graph. We have used Uniform Cost Search Algorithm to search within the AIG network, a specific AIG node order for the reordering rules application. After the reordering rules application, the AIG network is mapped to an AIG netlist using specific library cells. Our approach combines network re-structuring, AIG nodes reordering, dynamic power and longest path delay estimation and optimisation and finally technology mapping to an AIG netlist. A set of MCNC Benchmark circuits and large combinational circuits up to 100,000 gates have been used to validate our methodology. Comparisons for power and delay optimisation are made with the best synthesis scripts used in ABC. Reduction of 23% in power and 15% in delay with minimal overhead is achieved, compared to the best known ABC results. Also, our approach is also implemented on a number of processors with combinational and sequential components and significant savings are achieved.

Empirical analysis and improved modelling of natural gas demand in Ireland

Countries across the world are being challenged to decarbonise their energy systems in response to diminishing fossil fuel reserves, rising GHG emissions and the dangerous threat of climate change. There has been a renewed interest in energy efficiency, renewable energy and low carbon energy as policy‐makers seek to identify and put in place the most robust sustainable energy system that can address this challenge. This thesis seeks to improve the evidence base underpinning energy policy decisions in Ireland with a particular focus on natural gas, which in 2011 grew to have a 30% share of Ireland’s TPER. Natural gas is used in all sectors of the Irish economy and is seen by many as a transition fuel to a low-carbon energy system; it is also a uniquely excellent source of data for many aspects of energy consumption. A detailed decomposition analysis of natural gas consumption in the residential sector quantifies many of the structural drives of change, with activity (R2 = 0.97) and intensity (R2 = 0.69) being the best explainers of changing gas demand. The 2002 residential building regulations are subject to an ex-post evaluation, which using empirical data finds a 44 ±9.5% shortfall in expected energy savings as well as a 13±1.6% level of non-compliance. A detailed energy demand model of the entire Irish energy system is presented together with scenario analysis of a large number of energy efficiency policies, which show an aggregate reduction in TFC of 8.9% compared to a reference scenario. The role for natural gas as a transition fuel over a long time horizon (2005-2050) is analysed using an energy systems model and a decomposition analysis, which shows the contribution of fuel switching to natural gas to be worth 12 percentage points of an overall 80% reduction in CO2 emissions. Finally, an analysis of the potential for CCS in Ireland finds gas CCS to be more robust than coal CCS for changes in fuel prices, capital costs and emissions reduction and the cost optimal location for a gas CCS plant in Ireland is found to be in Cork with sequestration in the depleted gas field of Kinsale.

The application of computer modelling to develop a methodology for ‘existing building’ upgrades to move towards carbon-neutral buildings

The retrofitting of existing buildings for decreased energy usage, through increased energy efficiency and for minimum carbon dioxide emissions throughout their remaining lifetime is a major area of research. This research area requires development to provide building professionals with more efficient building retrofit solution determination tools. The overarching objective of this research is to develop a tool for this purpose through the implementation of a prescribed methodology. This has been achieved in three distinct steps. Firstly, the concept of using the degree-days modelling method as an adequate means of basing retrofit decision upon was analysed and the results illustrated that the concept had merit. Secondly, the concept of combining the degree-days modelling method and the Genetic Algorithms optimisation method is investigated as a method of determining optimal thermal energy retrofit solutions. Thirdly, the combination of the degree-days modelling method and the Genetic Algorithms optimisation method were packaged into a building retrofit decision-support tool and named BRaSS (Building Retrofit Support Software). The results demonstrate clearly that, fundamental building information, simplified occupancy profiles and weather data used in a static simulation modelling method is a sufficient and adequate means to base retrofitting decisions upon. The results also show that basing retrofit decisions upon energy analysis results are the best means to guide a retrofit project and also to achieve results which are optimum for a particular building. The results also indicate that the building retrofit decision-support tool, BRaSS, is an effective method to determine optimum thermal energy retrofit solutions.