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This research aims at linking Urban Planning, Energy Simulations and Climate Change projections into the year 2100 for hot climates. The workflow of going back and forth between urban and city scale plans and individual neighborhood parcels to building scale, for the sake of simulating energy demand for a given city into the future is complex. It is prone to rely on many assumptions and simplifications in order to aid the simulations. In this work, we streamline the process with new computational tools, with the goal of communicating a more precise impact of building scale and neighborhood morphological scale design and retrofit strategies in order to meet energy reduction and carbon emission targets focusing on 2030, 2050 and 2100. Urban scenarios are developed using Envision Tomorrow. The building archetypes used therein are associated with energy demand profiles which we simulate using EnergyPlus for various climate change scenarios to improve the forecasting ability of Envision Tomorrow. Denser developments yield far lower neighborhood energy use.

This research project compared a standard vapor compression system and a standard desiccant dehumidification system with heat wheel to determine if there was some potential energy savings for “shoulder season” hours in Austin Texas. “Shoulder season” hours as defined in the paper are hours during which the dry bulb temperature falls within the American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) comfort bound but the humidity is above the comfortable humidity point. These hours are normally addressed with vapor compression systems which dehumidify by cooling the air under the comfort setpoint to dehumidify, which is wasteful of energy. The study found that for these shoulder season hours a desiccant dehumidification system was around 4.5 times more energy efficient at reaching comfort setpoints if free heating was used for drying the desiccant.

We analyze 100 case studies, which were conducted in 23 countries, and contrast their data on embodied and operational energy in residential and commercial buildings. The case studies include conventional, retrofit, low-energy, passive, and net-zero energy buildings. The buildings have different lifetimes varying from 25 to 100 years. We calculate the estimated total Life Cycle Energy (LCE) as the sum of Embodied Energy (EE) and Operational Energy (OE). The LCE in the 100 case studies ranges from 50.8 to 1840 MJ/m2 per year. Our results show that operational energy significantly dominates the life cycle energy of the buildings by an average of 419 MJ/m2 per year and an average share of 72%. The share of embodied energy increases with increasing operational energy. However, the overall LCE decreases significantly when the operational energy decreases. Naturally, the assumptions on the lifetime of the buildings have a great impact on the LCE. We conclude that operational energy should be primarily reduced in order to decrease greenhouse gas emissions from the existing building stock because most of the buildings are already built and changes in the embodied energy are often obtained only through new construction or deep retrofit strategies. Depending on the strategy to decrease OE, the share of EE was found to show wide fluctuations within the case studies, ranging from 4% up to 100%. In addition, most of the operational energy consumption has been reported by using energy simulation tools. Only about 14% of the case studies had metered operational energy data. In order to create more accurate data, metering of buildings should be considered in future case studies., Journal of Physics: Conference Series, 1343, ISSN:1742-6588, ISSN:1742-6596

Building energy simulation helps governments implement effective policies to increase energy efficiency. In this work, we use deep neural networks (DNN) to create a surrogate model of an urban energy simulator. We modelled 7,860 buildings, with 2,620 geometries, and simulated them across all the climatic regions of the US. With these 68 million hourly data points, we trained two DNNs to predict the solar gains and thermal losses. The DNNs reduce computational time by a factor of 2500 while maintaining good accuracy (R2=0.85). Possible applications are prediction of energy demand due to climate change and building refurbishment measures.

This paper investigates different urban growth and densification scenarios and their energy performance under climate change conditions using urban energy simulation. We developed an abstract urban growth model based on the West Campus neighborhood in downtown Austin, Texas. Four different scenarios were applied to an abstract population growth model, which were then tested from the current year to 2100 under both climate change and control (TMY) conditions. Our results show that all urban growth models perform similarly in terms of operational energy but differ radically in terms of carrying capacity and embodied energy.

Increasing amounts of energy consumption data available to researchers in recent years make it easier to analyze the global energy demands. The analysis is important because buildings consume roughly 40% of global energy. One of the applications of data-driven analysis is identifying buildings that follow similar energy consumption patterns. Despite the number of available unsupervised machine learning algorithms, the user is still required to choose features, typically resorting to hourly data. In this paper, we propose we present SCAFE, an end-to-end architecture for Simultaneous Clustering And non-linear Feature Extraction. The method consists of first transforming the raw energy profiles that are represented as time-series into three channel images, using Gramian Summation, Gramian Difference and Markov Transition Fields. Then, we map the images to the latent space with the use of an autoencoder. Finally, we perform clustering in the latent space, simultaneously obtaining clusters and features of the dataset. We demonstrate SCAFE on the UTexas dataset, showing similar performance to standard k-means clustering while in addition also extracting natural features of the data in the latent space.

We define the elliptic quantum group $E_{\tau,\eta}(so_3)$ and the transfer matrix corresponding to its simplest highest weight representation. We use Bethe anstaz method to construct the creation operators as polynomials of the Lax matrix elements expressed through a recurrence relation. We give common eigenvectors and eigenvalues of the family of commuting transfer matrices., Comment: 13 pages

This paper presents the design, fabrication and calibration of a multi-axis micro force–torque sensor. The sensor and its readout electronics are specifically designed to simultaneously measure two force components and one torque component. The load is measured by capacitive comb drives which provide high sensitivity. The sensor is applied to measure micro-Newton level forces and nano-Newton-meter level torques on a magnetically actuated microrobot. This microrobot is assembled from microfabricated nickel parts and is designed for directed drug delivery inside the human body. The precise knowledge of the forces and torques will help design magnetic position controllers as well as understand the magnetic properties of the electroplated microparts.