This thesis develops methods for reducing energy Measurement and Verification (M&V) costs through the use of Bayesian statistics. M&V quantifies the savings of energy efficiency and demand side projects by comparing the energy use in a given period to what that use would have been, had no interventions taken place. The case of a large-scale lighting retrofit study, where incandescent lamps are replaced by Compact Fluorescent Lamps (CFLs), is considered. These projects often need to be monitored over a number of years with a predetermined level of statistical rigour, making M&V very expensive. M&V lighting retrofit projects have two interrelated uncertainty components that need to be addressed, and which form the basis of this thesis. The first is the uncertainty in the annual energy use of the average lamp, and the second the persistence of the savings over multiple years, determined by the number of lamps that are still functioning in a given year. For longitudinal projects, the results from these two aspects need to be obtained for multiple years. This thesis addresses these problems by using the Bayesian statistical paradigm. Bayesian statistics is still relatively unknown in M&V, and presents an opportunity for increasing the efficiency of statistical analyses, especially for such projects. After a thorough literature review, especially of measurement uncertainty in M&V, and an introduction to Bayesian statistics for M&V, three methods are developed. These methods address the three types of uncertainty in M&V: measurement, sampling, and modelling. The first method is a low-cost energy meter calibration technique. The second method is a Dynamic Linear Model (DLM) with Bayesian Forecasting for determining the size of the metering sample that needs to be taken in a given year. The third method is a Dynamic Generalised Linear Model (DGLM) for determining the size of the population survival survey sample. It is often required by law that M&V energy meters be calibrated periodically by accredited laboratories. This can be expensive and inconvenient, especially if the facility needs to be shut down for meter installation or removal. Some jurisdictions also require meters to be calibrated in-situ; in their operating environments. However, it is shown that metering uncertainty makes a relatively small impact to overall M&V uncertainty in the presence of sampling, and therefore the costs of such laboratory calibration may outweigh the benefits. The proposed technique uses another commercial-grade meter (which also measures with error) to achieve this calibration in-situ. This is done by accounting for the mismeasurement effect through a mathematical technique called Simulation Extrapolation (SIMEX). The SIMEX result is refined using Bayesian statistics, and achieves acceptably low error rates and accurate parameter estimates. The second technique uses a DLM with Bayesian forecasting to quantify the uncertainty in metering only a sample of the total population of lighting circuits. A Genetic Algorithm (GA) is then applied to determine an efficient sampling plan. Bayesian statistics is especially useful in this case because it allows the results from previous years to inform the planning of future samples. It also allows for exact uncertainty quantification, where current confidence interval techniques do not always do so. Results show a cost reduction of up to 66%, but this depends on the costing scheme used. The study then explores the robustness of the efficient sampling plans to forecast error, and finds a 50% chance of undersampling for such plans, due to the standard M&V sampling formula which lacks statistical power. The third technique uses a DGLM in the same way as the DLM, except for population survival survey samples and persistence studies, not metering samples. Convolving the binomial survey result distributions inside a GA is problematic, and instead of Monte Carlo simulation, a relatively new technique called Mellin Transform Moment Calculation is applied to the problem. The technique is then expanded to model stratified sampling designs for heterogeneous populations. Results show a cost reduction of 17-40%, although this depends on the costing scheme used. Finally the DLM and DGLM are combined into an efficient overall M&V plan where metering and survey costs are traded off over multiple years, while still adhering to statistical precision constraints. This is done for simple random sampling and stratified designs. Monitoring costs are reduced by 26-40% for the costing scheme assumed. The results demonstrate the power and flexibility of Bayesian statistics for M&V applications, both in terms of exact uncertainty quantification, and by increasing the efficiency of the study and reducing monitoring costs. / Hierdie proefskrif ontwikkel metodes waarmee die koste van energiemonitering en verifieëring (M&V) deur Bayesiese statistiek verlaag kan word. M&V bepaal die hoeveelheid besparings wat deur energiedoeltreffendheid- en vraagkantbestuurprojekte behaal kan word. Dit word gedoen deur die energieverbruik in ’n gegewe tydperk te vergelyk met wat dit sou wees indien geen ingryping plaasgevind het nie. ’n Grootskaalse beligtingsretrofitstudie, waar filamentgloeilampe met fluoresserende spaarlampe vervang word, dien as ’n gevallestudie. Sulke projekte moet gewoonlik oor baie jare met ’n vasgestelde statistiese akkuuraatheid gemonitor word, wat M&V duur kan maak. Twee verwante onsekerheidskomponente moet in M&V beligtingsprojekte aangespreek word, en vorm die grondslag van hierdie proefskrif. Ten eerste is daar die onsekerheid in jaarlikse energieverbruik van die gemiddelde lamp. Ten tweede is daar die volhoubaarheid van die besparings oor veelvoudige jare, wat bepaal word deur die aantal lampe wat tot in ’n gegewe jaar behoue bly. Vir longitudinale projekte moet hierdie twee komponente oor veelvoudige jare bepaal word. Hierdie proefskrif spreek die probleem deur middel van ’n Bayesiese paradigma aan. Bayesiese statistiek is nog relatief onbekend in M&V, en bied ’n geleentheid om die doeltreffendheid van statistiese analises te verhoog, veral vir bogenoemde projekte. Die proefskrif begin met ’n deeglike literatuurstudie, veral met betrekking tot metingsonsekerheid in M&V. Daarna word ’n inleiding tot Bayesiese statistiek vir M&V voorgehou, en drie metodes word ontwikkel. Hierdie metodes spreek die drie hoofbronne van onsekerheid in M&V aan: metings, opnames, en modellering. Die eerste metode is ’n laekoste energiemeterkalibrasietegniek. Die tweede metode is ’n Dinamiese Linieêre Model (DLM) met Bayesiese vooruitskatting, waarmee meter opnamegroottes bepaal kan word. Die derde metode is ’n Dinamiese Veralgemeende Linieêre Model (DVLM), waarmee bevolkingsoorlewing opnamegroottes bepaal kan word. Volgens wet moet M&V energiemeters gereeld deur erkende laboratoria gekalibreer word. Dit kan duur en ongerieflik wees, veral as die aanleg tydens meterverwydering en -installering afgeskakel moet word. Sommige regsgebiede vereis ook dat meters in-situ gekalibreer word; in hul bedryfsomgewings. Tog word dit aangetoon dat metingsonsekerheid ’n klein deel van die algehele M&V onsekerheid beslaan, veral wanneer opnames gedoen word. Dit bevraagteken die kostevoordeel van laboratoriumkalibrering. Die voorgestelde tegniek gebruik ’n ander kommersieële-akkuurraatheidsgraad meter (wat self ’n nie-weglaatbare metingsfout bevat), om die kalibrasie in-situ te behaal. Dit word gedoen deur die metingsfout deur SIMulerings EKStraptolering (SIMEKS) te verminder. Die SIMEKS resultaat word dan deur Bayesiese statistiek verbeter, en behaal aanvaarbare foutbereike en akkuurate parameterafskattings. Die tweede tegniek gebruik ’n DLM met Bayesiese vooruitskatting om die onsekerheid in die meting van die opnamemonster van die algehele bevolking af te skat. ’n Genetiese Algoritme (GA) word dan toegepas om doeltreffende opnamegroottes te vind. Bayesiese statistiek is veral nuttig in hierdie geval aangesien dit vorige jare se uitslae kan gebruik om huidige afskattings te belig Dit laat ook die presiese afskatting van onsekerheid toe, terwyl standaard vertrouensintervaltegnieke dit nie doen nie. Resultate toon ’n kostebesparing van tot 66%. Die studie ondersoek dan die standvastigheid van kostedoeltreffende opnameplanne in die teenwoordigheid van vooruitskattingsfoute. Dit word gevind dat kostedoeltreffende opnamegroottes 50% van die tyd te klein is, vanweë die gebrek aan statistiese krag in die standaard M&V formules. Die derde tegniek gebruik ’n DVLM op dieselfde manier as die DLM, behalwe dat bevolkingsoorlewingopnamegroottes ondersoek word. Die saamrol van binomiale opname-uitslae binne die GA skep ’n probleem, en in plaas van ’n Monte Carlo simulasie word die relatiewe nuwe Mellin Vervorming Moment Berekening op die probleem toegepas. Die tegniek word dan uitgebou om laagsgewyse opname-ontwerpe vir heterogene bevolkings te vind. Die uitslae wys ’n 17-40% kosteverlaging, alhoewel dit van die koste-skema afhang. Laastens word die DLM en DVLM saamgevoeg om ’n doeltreffende algehele M&V plan, waar meting en opnamekostes teen mekaar afgespeel word, te ontwerp. Dit word vir eenvoudige en laagsgewyse opname-ontwerpe gedoen. Moniteringskostes word met 26-40% verlaag, maar hang van die aangenome koste-skema af. Die uitslae bewys die krag en buigsaamheid van Bayesiese statistiek vir M&V toepassings, beide vir presiese onsekerheidskwantifisering, en deur die doeltreffendheid van die dataverbruik te verhoog en sodoende moniteringskostes te verlaag. / Thesis (PhD)--University of Pretoria, 2017. / National Research Foundation / Department of Science and Technology / National Hub for the Postgraduate Programme in Energy Efficiency and Demand Side Management / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

B-cell chronic lymphocytic leukemia (B-CLL) is the most common leukemia among the adult population in western countries and accounts for 30-40% of all leukemias. With survival time ranging from months to decades, the clinical course of individual CLL patients is highly variable. This heterogeneity and in the end the need for means to identify the patients with less favorable disease has encouraged the search for biomarkers that can predict the prognosis. Telomeres are repetitive structures protecting the chromosomal endings and shorten at each cell division. Telomere length (TL) has been indicated as a prognostic factor both in hematological malignancies and solid tumors. In B-CLL, TL is associated with mutation status of the immunoglobulin heavy chain variable (IGHV) gene and with clinical course. In the present thesis the main aim was to evaluate TL as a biomarker in B-CLL using a quantitative PCR-based method for TL determination. In paper I, TL was shown to be a prognostic factor for stage A and stage B/C patients, whereas IGHV mutation status predicted outcome only in stage A patients. Moreover, IGHV mutated CLL cases were subdivided by TL into two groups with different prognosis, a subdivision not seen for unmutated cases. Interestingly, the IGHV-mutated group with short telomeres had en overall survival close to that of the unmutated cases. Thus, a combination of IGHV mutation status and telomere length gave an improved subclassification of CLL identifying previously unrecognized patient groups with different outcomes. TL correlates with cellular origin of B-cell malignancies in relation to the germinal center (GC). In paper II different B-cell lymphoma/leukemia subtypes were analyzed. Shortest telomeres were found in IGHV unmutated CLLs, differing significantly from IGHV mutated cases. Contrary to this, mantle cell lymphomas (MCL) demonstrated similar TL regardless of IGHV mutation status. TL differed significantly between GC-like and non-GC-like diffuse large B-cell lymphomas (DLBCL) and follicular lymphomas (FL) had shorter telomeres than GC-like DLBCL. Hairy cell leukemias, which display Ig gene intraclonal heterogeneity, had longer telomeres than FLs and non-GC-DLBCL, but shorter than GC-DLBCL. In conclusion, TL seemed not to simply correlate with GC origin. Paper III presents a B-CLL cohort assessed for TL, genomic aberrations, IGHV mutation status, CD38 and ZAP-70 expression. An inverse correlation existed between TL and IGHV homology, CD38 and ZAP-70 expression. The presence of genomic aberrations was similar among patients regardless of TL. In contrast, 13q deletion, a favorable biomarker, was more frequent in patients with long telomeres, while 11q and 17p deletions (markers of less favorable outcome) were more frequent in the subgroup with short telomeres. In paper IV a large group of mainly indolent CLL cases from a population based cohort was studied again showing an association between TL and prognosis, especially in “good” prognosis cases as defined by other biomarkers. Multivariate analysis indicated a strong connection between IGHV mutation status, lipoprotein lipase (LPL) expression and TL. A comparison of TL in diagnostic and follow up samples demonstrated a significant correlation, and also in the follow samples TL constituted a significant biomarker for survival.

An improved model for reducing the cost of long-term monitoring in Clean Development Mechanism (CDM) lighting retrofit projects is proposed. Cost-effective longitudinal sampling designs use the minimum number of meters required to report yearly savings at the 90% confidence and 10% relative precision level for duration of the project (up to 10 years) as stipulated by the CDM. Improvements to the existing model include a new non-linear Compact Fluorescent Lamp population decay model based on the results of the Polish Efficient Lighting Project, and a cumulative sampling function modified to weight samples exponentially by recency. An economic model altering the cost function to a nett present value calculation is also incorporated. The search space for such sampling models are investigated and found to be discontinuous and stepped, requiring a heuristic for optimisation; in this case the Genetic Algorithm was used. Assuming an exponential smoothing rate of 0.25, an inflation rate of 6.44%, and an interest rate of 10%, results show that sampling should be more evenly distributed over the study duration than is currently considered optimal, and that the proposed improvements in model accuracy increase expected project costs in nett present value terms by approximately 20%. A sensitivity analysis reveals that the expected project cost is most sensitive to the reporting precision level, coefficient of variance, and reporting / Dissertation (MEng)--University of Pretoria, 2014. / gm2014 / Electrical, Electronic and Computer Engineering / Unrestricted