This study aims at a preliminary characterization of system operation uncertainty. It bases this on an analysis of the energy consumption of 6 existing buildings on the Georgia Tech campus. The analysis is speculative in nature.

There is widespread acceptance of the need to reduce energy consumption within the built environment. Despite this, there are often large discrepancies between the energy performance aspiration and operational reality of modern buildings. The application of existing mitigation measures appears to be piecemeal and lacks a whole-system approach to the problem. This Engineering Doctorate aims to identify common reasons for performance discrepancies and develop a methodology for risk mitigation. Existing literature was reviewed in detail to identify individual factors contributing to the risk of a building failing to meet performance aspirations. Risk factors thus identified were assembled into a taxonomy that forms the basis of a methodology for identifying and evaluating performance risk. A detailed case study was used to investigate performance at whole-building and sub-system levels. A probabilistic approach to estimating system energy consumption was also developed to provide a simple and workable improvement to industry best practice. Analysis of monitoring data revealed that, even after accounting for the absence of unregulated loads in the design estimates, annual operational energy consumption was over twice the design figure. A significant part of this discrepancy was due to the space heating sub-system, which used more than four times its estimated energy consumption, and the domestic hot water sub-system, which used more than twice. These discrepancies were the result of whole-system lifecycle risk factors ranging from design decisions and construction project management to occupant behaviour and staff training. Application of the probabilistic technique to the estimate of domestic hot water consumption revealed that the discrepancies observed could be predicted given the uncertainties in the design assumptions. The risk taxonomy was used to identify factors present in the results of the qualitative case study evaluation. This work has built on practical building evaluation techniques to develop a new way of evaluating both the uncertainty in energy performance estimates and the presence of lifecycle performance risks. These techniques form a risk management methodology that can be applied usefully throughout the project lifecycle.

Increasing studies imply that predicted energy performance of buildings significantly deviates from actual measured energy use. This so-called "performance gap" may undermine one's confidence in energy-efficient buildings, and thereby the role of building energy efficiency in the national carbon reduction plan. Closing the performance gap becomes a daunting challenge for the involved professions, stimulating them to reflect on how to investigate and better understand the size, origins, and extent of the gap. The energy performance gap underlines the lack of prediction capability of current building energy models. Specifically, existing predictions are predominantly deterministic, providing point estimation over the future quantity or event of interest. It, thus, largely ignores the error and noise inherent in an uncertain future of building energy consumption. To overcome this, the thesis turns to a thriving area in engineering statistics that focuses on computation-based uncertainty quantification. The work provides theories and models that enable probabilistic prediction over future energy consumption, forming the basis of risk assessment in decision-making. Uncertainties that affect the wide variety of interacting systems in buildings are organized into five scales (meteorology - urban - building - systems - occupants). At each level both model form and input parameter uncertainty are characterized with probability, involving statistical modeling and parameter distributional analysis. The quantification of uncertainty at different system scales is accomplished using the network of collaborators established through an NSF-funded research project. The bottom-up uncertainty quantification approach, which deals with meta uncertainty, is fundamental for generic application of uncertainty analysis across different types of buildings, under different urban climate conditions, and in different usage scenarios. Probabilistic predictions are evaluated by two criteria: coverage and sharpness. The goal of probabilistic prediction is to maximize the sharpness of the predictive distributions subject to the coverage of the realized values. The method is evaluated on a set of buildings on the Georgia Tech campus. The energy consumption of each building is monitored in most cases by a collection of hourly sub-metered consumption data. This research shows that a good match of probabilistic predictions and the real building energy consumption in operation is achievable. Results from the six case buildings show that using the best point estimations of the probabilistic predictions reduces the mean absolute error (MAE) from 44% to 15% and the root mean squared error (RMSE) from 49% to 18% in total annual cooling energy consumption. As for monthly cooling energy consumption, the MAE decreases from 44% to 21% and the RMSE decreases from 53% to 28%. More importantly, the entire probability distributions are statistically verified at annual level of building energy predictions. Based on uncertainty and sensitivity analysis applied to these buildings, the thesis concludes that the proposed method significantly reduces the magnitude and effectively infers the origins of the building energy performance gap.

The successful delivery of low-carbon housing (both new and retrofitted) is a key aspect of the UK s commitment to an 80% reduction in carbon emissions by 2050. In this context, the inclusion of small-scale building-integrated renewable energy technologies is an important component of low carbon design strategies, and is subject to numerous regulation and incentive schemes (including the Renewable Heat Incentive (RHI)) set up by government to encourage uptake and set minimum performance benchmarks. Unfortunately there are numerous examples of in-use energy and carbon performance shortfalls for new and retrofitted buildings this is termed the performance gap . Technical and human factors associated with building subsystem performance, which are often not considered in design tools used to predict performance, are the root cause of performance uncertainty. The research presented in this doctoral thesis aims to develop and apply a novel probabilistic method of evaluating the performance uncertainty of solar thermal systems installed in the UK. Analysis of measured data from a group of low carbon retrofitted dwellings revealed that the majority of buildings failed to meet the designed-for carbon emissions target with an average percentage difference of 60%. An in-depth case study technical evaluation of one of these dwellings showed significant dysfunction associated with the combined ASHP/solar thermal heating system, resulting in a performance gap of 94%, illustrating that the performance gap can be regarded as a whole-system problem, comprising a number of subsystem causal factors. Using a detailed dataset obtained from the UK s largest field trial of domestic solar thermal systems, a cross-cutting evaluation of predicted vs. measured performance similarly revealed a discrepancy with a mean percentage difference in predicted and measured annual yield of -24%. Having defined the nature and extent of underperformance for solar thermal technology in the UK, causal factors influencing performance were mapped and the associated uncertainty quantified using a novel knowledge-based Bayesian network (BN). In addition, the BN approach along with Monte Carlo sampling was applied to the well-established BREDEM model in order to quantify performance uncertainty of solar thermal systems by producing distributions of annual yield. As such, the modified BN-based BREDEM model represents a significant improvement in the prediction of performance of small-scale renewable energy technologies. Finally, financial analysis applied to the probabilistic predictions of annual yield revealed that the current UK RHI scheme is unlikely to result in positive returns on investment for solar thermal systems unless the duration of the payments is extended or electricity is the primary source of heating.

There is a need to reduce unsustainable use of fossil fuels. Increased usage of renewable energy by combined use of photovoltaic solar panels (PV) with battery storage is one way. Another way is to increase awareness of energy usage and reduce the energy performance gap by building energy-efficient buildings. Buildings have a long lifetime and high energy usage will have an impact for a long time. Barriers, drivers and non-energy benefits (NEBs) for investments in battery storage in photovoltaic systems (PV) in the context of farmers in Sweden with PV systems was investigated by a questionnaire study. The questionnaire was sent to farmers in Sweden who already have photovoltaics installed and about 100 persons answered, a response rate of 59%. Among the drivers for investments in battery storage in PV systems in agriculture it was found that the highest-ranked driver, i.e., to use a larger part of the electricity produced oneself, turns out to be the highest priority for grid owners seeking to reduce the need for extensive investments in the grid. The primary NEBs found were the possibility to become more independent of grid electricity. A method for the building process, called ByggaE, which aims to reduce the energy performance gap, has been developed and described. The method is based on two main processes with activities. Documents that support the activities can be found and stored in the energy documentation, a digital map structure. The two main processes are: The client’s activity to formulate requirements and ways to verify these requirements. The main process for other actors is to identify, handle and follow up risks or critical parts. An overall relation between the energy efficiency gap and the energy performance gap has been identified. Realistic assumptions and follow-up related to the assumptions are found to be important to reduce both the energy efficiency gap and the energy performance gap. / För att uppnå klimatmålen är det nödvändigt att minska den ohållbara användningen av fossila bränslen. Ett sätt är att öka användning av förnybar energi genom att kombinera solel med batterilager. Ett annat sätt är att öka medvetenheten om energianvändningen med dess negativa påverkan på miljön och uppfylla energikraven för nya byggnader bättre. Eftersom byggnader har lång livslängd ger onödigt hög energianvändning påverkan under lång tid.   Hinder, drivkrafter och andra icke energirelaterade fördelar med att investera i batterilager till solel har undersökts i en enkätstudie bland svenska lantbruk. Det kom in 100 svar från lantbrukare som har solel, vilket motsvarar en svarsfrekvens på 59 %. Den viktigaste drivkraften för att investera i batterilager till solelanläggningen är en högre egenanvändning av el. Detta visade sig också vara högst prioriterat av elnätsägare för att minska behovet av kostsamma investeringar i elnätet. Den största icke energirelaterade fördelen med batterilager är större oberoende av elnätet.   En kvalitetsäkringsmetod för byggprocessen har utvecklats och beskrivits. Syftet med metoden, som kallas ByggaE, är att minska skillnaden mellan verklig energianvändning och energikrav i nya byggnader. Metoden bygger på två huvudprocesser med aktiviteter. Beställarens huvudprocess är att formulera krav och metoder att kontrollera och följa upp dessa krav. De andra aktörernas huvudprocess är att identifiera, hantera och följa upp risker eller kritiska moment som kan påverka energianvändningen. Dokument som stödjer aktiviteterna lagras i en digital mappstruktur.   Det är viktigt med realistiska antaganden och uppföljning som relaterar till dessa antaganden för att fler lönsamma energieffektiviseringsåtgärder ska bli genomförda och för att de energiprestanda som krävs eller förväntas ska bli uppfyllda.