Temporal response of the human visual system to suprathreshold luminance and opponent colour contrast gratings

The goal of the present study was to characterize the temporal processing of both suprathreshold luminance and opponent-colour defined contrast in the human visual system. We used a detection task in five experiments; following a 900 Hz, 2-cycle tone, observers were presented with a sinusoidal grating stimulus. The interval separating the waming tone and the presentation of the grating was manipulated to determine the influences of attentional dwell time in a cross-modal task. This theory states that the first of two successive events will interfere with the processing of the second event. In all four luminance experiments the gratings were presented with contrast levels of 8, 16 and 64 percent contrast. In the first experiment the contrast gratings were presented following interstimulus intervals (lSls) ranging from 100 to 1000 ms in 100 ms intervals. In Experiment 2 the lSls were 1000 ms and 1500 ms. In the third experiment gratings were presented following lSls ranging from 500 to 2000 ms in 500 ms intervals. In Experiment 4 gratings were presented following lSls ranging from 250 to 1000 ms in 250 ms intervals. In the opponent-colour experiment the gratings were presented with colour contrasts of red-green (R6), blue-yellow (BY) or red-blue (RB) following lSls of 100, 250, 500, and 750 ms. In all experiments the mean luminance of the gratings and the grey background was 10 cd/m2. Reaction times (RTs) were used to measure the latency difference in processing these gratings. The findings demonstrated that increases in suprathreshold contrast resulted in a significant decrease in response latency. In addition, we found decreased RTs for RG as opposed to BY and RB gratings. Finally, we were able to demonstrate an attentional dwell time in a cross-modal task. The implications of the above findings were discussed in conjunction with the relevant literature

Achieving Faster Building Energy Model Optimization through Selective Zone Elimination

Optimization in building performance simulation (BPS) has become increasingly important due to the growing need for high-performance building design and operation. Numerous research efforts have been dedicated to decreasing optimization runtime by introducing improved optimization algorithms and advanced sampling techniques. This paper presents a novel model order reduction (MOR) algorithm tailored for speeding up building energy simulation. The algorithm identifies archetype zones simplifying the needless repetition of thermal zones. For an entire optimization process, this MOR method can be repeated recursively to reproduce reduced models. The proposed method can be used to speed up large-scale simulations including optimization, uncertainty analysis and model predictive controls. Preliminary results with parametric simulations show a runtime reduction of about 76% reduction for 15 simulations while still maintaining the predicted total annual energy consumption within a 10% margin. Further research will be conducted to compare the optimization results when applying the proposed MOR algorithm and determine if the reduced model produces the same optimal design. The proposed method may significantly improve the optimization runtime with a minor effect on optimization accuracy, thus increasing the overall usability of BPS optimizations

Pathways to Net-Zero Energy Buildings: An Optimization Methodology

Building Performance Simulation (BPS) is frequently used by decision-makers to estimate building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error methods of building simulation are used to identify combinations of parameters to reduce energy use. Optimization techniques combined with BPS offer many benefits such as: (i) identification of potential optimal designs which best achieve desired performance objectives; (ii) system level component integration by simultaneously considering conflicting trade-offs; and (iii) a process-oriented simulation tool that is complementary to BPS, eliminating the need for repetitive userinitiated model evaluations. However, the capability of optimization algorithms to effectively map out the entire solution space and discover information is farther reaching than building design. As shown in this thesis, optimization datasets are also a valuable resource for conducting uncertainty and sensitivity analyses and evaluating policies to incentivize low-energy building design. Two performance criteria are considered in this thesis: net-energy consumption and life-cycle cost. The term ‘performance-optimized’ refers to the extreme of these two criteria that is Net-Zero Energy (NZE) and cost-optimized buildings. A Net-Zero Energy Building (NZEB) generates at least as much renewable energy on-site as it consumes in a given year. A cost-optimized building has the lowest life-cycle cost over a considered period. A focus of this thesis is identifying optimal pathways to NZE and cost-optimized building designs. This thesis proposes the following approaches to identify pathways to net-zero energy: (i) a redesign case-study of an existing near-Net-Zero Energy Home (NZEH) archetype using a proposed optimization methodology; (ii) the development of an information-driven hybrid evolutionary algorithm for optimal building design; (iii) a methodology for identifying the influence of design variations on building energy performance; (iv) a methodology to evaluate the effect of incentives on life-cycle energy-cost curves; and (v) effect of a time-of-use feed-in tariff on optimal net-zero energy home design. The optimization methodology consists of: (i) an energy model; (ii) a cost model; (iii) a custom optimization algorithm; (iv) a database; and (v) a statistics module. Several new simulation techniques are proposed to identify pathways to performanceoptimized net-zero energy buildings: (i) probability distribution functions extracted from previous simulations; (ii) back-tracking searches; and (iii) importance factors to summarize back-tracking search results. This thesis provides valuable information related to: (i) the development of performancebased energy codes for buildings; (ii) systematic design of cost-optimized NZEHs; (iii) systematic analysis of the impact of different design parameters on energy consumption and cost; (iv) the study of incentive measures for NZEHs

Optimization under economic uncertainty using a net zero energy commercial office case study

Energy modeling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves uncertainties such as predicting occupant behavior, future climate, and econometric parameters. As presently practiced, energy modelers typically do not quantify the implications of these unknowns into performance outcomes. This paper describes an energy modeling approach to quantify economic risk and better inform decision makers of the economic feasibility of a project. The proposed methodology suggests how economic uncertainty can be quantified within an optimization framework. This approach improves modeling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results. The methodology is demonstrated using a net zero energy commercial office building case study located in London, ON, Canada

Energy Modelling Methodology for Community Masterplanning

Net-zero energy is an influential idea in guiding the building stock towards renewable energy resources. Increasingly, this target is scaled to entire communities which may include dozens of buildings in each new development phase. Although building energy modelling processes and codes have been well developed to guide decision making, there is a lack of methodologies for community integrated energy masterplanning. The problem is further complicated by the availability of district systems which better harvest and store on-site renewable energy. In response to these challenges, this paper contributes an energy modelling methodology which helps energy masterplanners determine trade-offs between building energy saving measures and district system design. Furthermore, this paper shows that it is possible to mitigate electrical and thermal peaks of a net-zero energy community using minimal district equipment. The methodology is demonstrated using a cold-climate case-study with both significant heating/ cooling loads and solar energy resources

The Avdat in Late Antiquity Project: uncovering the Early Islamic phases of a Byzantine town in the Negev Highlands

Three seasons of archaeological fieldwork by the Avdat in Late Antiquity Project have yielded new evidence of intensive Early Islamic activity in the late antique town of Avdat in Israel's Negev Highlands. This evidence has important implications for understanding the fate of such towns in the region during the Byzantine–Islamic transition.British Academy Newton International Fellowshi

Distributed evolutionary algorithm for co-optimization of building and district systems for early community energy masterplanning

Buildings play a significant role in climate change mitigation. In North America, energy used to construct and operate buildings accounts for some 40% of total energy use, largely originating from fossil fuels. The strategic reduction of these energy demands requires knowledge of potential upgrades prior to a building's construction. Furthermore, renewable energy generation integrated into buildings façades and district systems can improve the resiliency of community infrastructure. However, loads that are non-coincidental with on-site generation can cause load balancing issues. This imbalance is typically due to solar resources peaking at noon, whereas building loads typically peak in the morning and late afternoon or evenings. Ideally, the combination of on-site generation and localized storage could remedy such load balancing issues while reducing the need for fossil fuels. In response to these issues, this paper contributes a methodology that co-optimizes building designs and district technologies as an integrated community energy system. A distributed evolutionary algorithm is proposed that can navigate over 10154 potential community permutations. This is the first time in literature that a methodology demonstrates the co-optimization of buildings and district energy systems to reduce energy use in buildings and balance loads at this scale. The proposed solution is reproducible and scalable for future community masterplanning studies

Methodology for energy and economic modeling of net zero energy communities

Net zero energy (NZE) communities are becoming pivotal to the energy vision of developers. Communities that produce as much energy as they consume provide many benefits, such as reducing life-cycle costs and better resilience to grid outages. If deployed using smart-grid technology, NZE communities can act as a grid node and aid in balancing electrical demand. However, identifying cost-effective pathways to NZE requires detailed energy and economic models. Information required to build such models is not typically available at the early master-planning stages, where the largest energy and economic saving opportunities exist. Methodologies that expedite and streamline energy and economic modeling could facilitate early decision making. This paper describes a reproducible methodology that aids modelers in identifying energy and economic savings opportunities in the early community design stages. As additional information becomes available, models can quickly be recreated and evaluated. The proposed methodology is applied to the first-phase design of a NZE community under development in Southwestern Ontario

Multi-objective optimal design of a near net zero energy solar house

This paper presents a multi-objective redesign case study of an archetype solar house based on a near net zero energy (NZE) demonstration home located in Eastman, Quebec. Using optimization techniques, pathways are identified from the original design to both cost and energy optimal designs. An evolutionary algorithm is used to optimize trade-offs between passive solar gains and active solar generation, using two objective functions: net-energy consumption and life-cycle cost over a thirty-year life cycle. In addition, this paper explores different pathways to net zero energy based on economic incentives, such as feed-in tariffs for on-site electricity production from renewables. The main objective is to identify pathways to net zero energy that will facilitate the future systematic design of similar homes based on the concept of the archetype that combines passive solar design; energy-efficiency measures, including a geothermal heat pump; and a building-integrated photovoltaic system. Results from this paper can be utilized as follows: (1) systematic design improvements and applications of lessons learned from a proven NZE home design concept, (2) use of a methodology to understand pathways to cost and energy optimal building designs, and (3) to aid in policy development on economic incentives that can positively influence optimized home design