The Impact of Different Building Assemblies on Thermal and Air Quality Performance: An Experimental Student Project in an Architectural Course Comparing City Building Code Versus Passivhaus Standard

Building technology can promote environmental sustainability and is a critical component of energy conservation. Well-functioning indoor microclimates do not only affect buildings, however—occupant health must also be considered. Recent research has demonstrated that sustainability and occupant health go hand-in-hand when designing or retrofitting a building. What is the best way to accomplish these goals and to effectively preserve a balance among architecture, efficiency, and health when designing and constructing new buildings? What is then the best approach to integrating this building technology knowledge into an architecture course? This research-educational project introduced an investigation of the effects of two different building assembly strategies (located in the hot and humid climate of San Antonio, TX) to a building science course. With faculty guidance, architecture students designed (and will build next semester) two distinctive testbeds following the San Antonio building code and Passivhaus standard, a standard focused on energy efficiency and healthy architecture. The indoor air temperatures, relative humidity levels, and R-values of both structures were simulated and compared during class. The effects of the outdoor environment parameters like air temperature, relative humidity, solar radiation, and wind speed were also explored. The results underline the positive effect of the Passivhaus standard envelope, which considerably reduces the indoor temperature as compared to the city code assembly. Additionally, indoor air quality parameters such as carbon dioxide (CO2), particle concentration (PM2.5 and PM10), total volatile organic compounds (TVOC) and formaldehyde (HCHO), will be also measured and analyzed once the building phase is complete

Intelligent signal analysis methodologies for nuclear detection, identification and attribution

Detection and identification of special nuclear materials can be fully performed with a radiation detector-spectrometer. Due to several physical and computational limitations, development of fast and accurate radioisotope identifier (RIID) algorithms is essential for automated radioactive source detection and characterization. The challenge is to identify individual isotope signatures embedded in spectral signature aggregation. In addition, background and isotope spectra overlap to further complicate the signal analysis. These concerns are addressed, in this thesis, through a set of intelligent methodologies recognizing signature spectra, background spectrum and, subsequently, identifying radionuclides. Initially, a method for detection and extraction of signature patterns is accomplished by means of fuzzy logic. The fuzzy logic methodology is applied on three types of radiation signal processing applications, where it exhibits high positive detection, low false alarm rate and very short execution time, while outperforming the maximum likelihood fitting approach. In addition, an innovative Pareto optimal multiobjective fitting of gamma ray spectra using evolutionary computing is presented. The methodology exhibits perfect identification while performs better than single objective fitting. Lastly, an innovative kernel based machine learning methodology was developed for estimating natural background spectrum in gamma ray spectra. The novelty of the methodology lies in the fact that it implements a data based approach and does not require any explicit physics modeling. Results show that kernel based method adequately estimates the gamma background, but algorithm\u27s performance exhibits a strong dependence on the selected kernel

Day-ahead electricity price forecasting using optimized multiple-regression of relevance vector machines

In deregulated, auction-based, electricity markets price forecasting is an essential participant tool for developing bidding strategies. In this paper, a day-ahead intelligent forecasting method for electricity prices is presented. The proposed approach is comprised of two steps. In the first step, a set of two relevance vector machines (RVM) is employed where each one provides next day predictions for the price evolution. In the second step, a multiple regression model comprised of the two relevance vector machines is built and the regression coefficients are computed using genetic based optimization. The performance of the proposed approach is tested on a set of electricity price hourly data from four different seasons and compared to those obtained by each of the relevance vector machines. The results clearly demonstrate, in terms of mean square error, the superiority of the proposed method over each individual RVM

Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed

Robust forecasting of wind speed values is a key element to effectively accommodate renewable generation from wind in smart power systems. However, the stochastic nature of wind and the uncertainties associated with it impose high challenge in its forecasting. A new method for forecasting wind speed in renewable energy generation is introduced in this study. The goal of the method is to provide a forecast in the form of an interval, which is determined by a mean value and the variance around the mean. In particular, the forecasting interval is produced according to a two‐step process: in the first step, a set of individual kernel modelled Gaussian processes (GP) are utilised to provide a respective set of interval forecasts, i.e. mean and variance values, over the future values of the wind. In the second step, the individual forecasts are evaluated using a fuzzy driven multiplexer, which selects one of them. The final output of the methodology is a single interval that has been identified as the best among the GP models. The presented methodology is tested on the set of real‐world data and benchmarked against the individual GPs as well as the autoregressive moving average model

Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition