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Impact of adaptive comfort criteria and heat waves on optimal building thermal mass control

ENERGY AND BUILDINGS 2007年第2期39卷 221-235页

作者： Henze, Gregor P. Pfafferott, Jens Herkel, Sebastian Felsmann, Clemens Fraunhofer Inst Solar Energy Syst D-79110 Freiburg Germany Tech Univ Dresden Inst Thermodynam & Bldg Syst Engn D-01069 Dresden Germany

This article investigates building thermal mass control of commercial buildings to reduce utility costs with a particular emphasis on the individual impacts of both adaptive comfort criteria and of heat waves. Recent changes in international standards on thermal comfort for indoor environments allow for adaptation to the weather development as manifested in comfort criteria prEN 15251.2005 and NPR-CR 1752.2005 relative to the non-adaptive comfort criterion ISO 7730.2003. Furthermore, since extreme weather patterns tend to occur more frequently, even in moderate climate zones, it is of interest how a building's passive thermal storage inventory responds to prolonged heat waves. The individual and compounded effects of adaptive comfort criteria and heat waves on the conventional and optimal operation of a prototypical office building are investigated for the particularly hot month of August 2003 in Freiburg, Germany. It is found that operating commercial buildings using adaptive comfort criteria strongly reduces total cooling loads and associated building systems energy consumption under conventional and building thermal mass control. In the case of conventional control, total operating cost reductions follow the cooling loads reductions closely. Conversely, the use of adaptive comfort criteria under optimal building thermal mass control leads to both lower and slightly higher absolute operating costs compared to the optimal costs for the non-adaptive ISO 7730. While heat waves strongly affect the peak cooling loads under both conventional and optimal building thermal mass control, total cooling loads, building energy consumption and costs are only weakly affected for both control modes. Passive cooling under cost-optimal control, while achieving significant total cost reductions of up to 13%, is associated with total energy penalties on the order of 1-3% relative to conventional nighttime setup control. Thus, building thermal mass control defends its cost saving pote

关键词： low-energy office buildings thermal comfort adaptive comfort models cooling energy thermal mass control night ventilation weather data analysis heat waves

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Correlating Extreme weather Conditions With Road Traffic Safety: A Unified Latent Space Model

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IEEE ACCESS 2022年 10卷 73005-73018页

作者： Fior, Jacopo Cagliero, Luca Politecn Torino I-10129 Turin Italy

The presence of extreme weather conditions is known to expose drivers to a higher risk to incur in road accidents. Quantifying the correlation between adverse weather conditions and road traffic safety is useful for several reasons such as planning preventive actions, managing vehicle fleets, and configuring alerting systems. However, since the risk of road accidents occurrences within a specific spatial region is influenced by several factors other than the weather conditions, quantifying the actual impact of adverse weather phenomena regardless of the effect of weather-unrelated conditions can be challenging. To tackle the aforesaid issue, this paper proposes to adopt a unified latent space model based on time series embeddings. Firstly, it encodes a subset of historical series reporting weather-related accident occurrences in specific risky areas into the high-dimensional vector representation. It also encodes the weather element measurements acquired by meteorological stations spread over the analyzed area. Then, to estimate the risk level of each region within the same spatial context it seeks the temporal risk patterns that are most similar to those observed in risky areas. The experiments carried out in a real case study confirm the applicability of the proposed approach.

关键词： Meteorology Road accidents Roads Time series analysis Safety Predictive models Area measurement Time series embeddings weather data analysis road accidents analysis key performance indicators

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Multithreaded Implementation of data Intensive Applications with Overlapped I/O and Computation

Multithreaded Implementation of Data Intensive Applications ...

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National Conference on Parallel Computing Technologies (PARCOMPTECH)

作者： Raju, K. Chiplunkar, Niranjan N. Karanth, Sahana NMAMIT Dept CS&E Karkala Karnataka India NMAMIT Karkala Karnataka India

ISBN: (纸本)9781479969180

The performance of parallel systems is limited by the I/O performance rather than the CPU performance. Scientific computing and database applications demand for large volume of I/O especially the disk I/O. The widening gap between CPU and disk speeds limits the speedup achieved by parallelizing such I/O intensive applications. Hence reducing I/O overhead in parallel systems deserves special attention. In this paper we introduce a technique that overlaps the disk I/O and computation for an OpenMP based parallel processing environment. The technique uses multiple threads that perform computation in parallel and a single thread for I/O. The overlapping is enabled by using pre-fetch buffers for every computational thread. The above method is tested for a weather data analysis application and the experimental results show that this approach can achieve a speed up of 1.75 over a single threaded implementation. Our system achieves a speed up as high as 8.71 over the Hadoop implementation of the same application.

关键词： Parallel systems overlapping of disk I/O and computation pre-fetch buffers OpenMP weather data analysis Hadoop

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Correlation Among Environmental Parameters using An Online Smart weather Station System 12

Correlation Among Environmental Parameters using An Online S...

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12 IEEE Int C Elect Energy Env Communications Computer Control

作者： Mittal, Yash Mittal, Anshika Bhateja, Diksha Parmaar, Komal Mittal, V. K. Jaypee Univ Informat Technol Solan India LNM Inst Informat Technol Jaipur Rajasthan India Indian Inst Informat Technol Chittoor Sri City India

ISBN: (纸本)9781467365406

In recent years, the evolving wireless technology, cheaper micro-controllers, smart cities concept and 'Internet of Things' (IoTs) have given way to the need of online wireless management systems for smart weather stations (SWS). In this paper, we develop an 'Online Smart weather Station System' for studying the correlation amongst multiple weather parameters data, collected over a period of 18 months. The system consists of sensors that generate weather data, local storage, wireless transfer to control centre, and web-based online representation and analysis of data. Six weather parameters such as air temperature, relative humidity, wind-speed, rain, rain-rate and solar energy are studied. Statistical measures such as mean, median, standard deviation, min, max and normalized standard deviation are computed from the weather data. The correlation among temperature, humidity and wind-speed parameters is presented. Few important observations are made, for possible applications in agriculture, construction and manufacturing activities. Further a web-based service can be aimed at automated analysis of weather data generated by the smart weather station.

关键词： smart weather station correlation of weather parameters weather data analysis web-based platform for SWS

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Study of time series algorithms for real-time weather prediction for a Gamma-ray Experiment 30

Study of time series algorithms for real-time weather predic...

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30th International Conference on High Performance Computing, data, and Analytics (HiPC)

作者： Sarkar, Debangana Chouhan, Nilesh Yadav, Kuldeep Homi Bhabha Natl Inst Bhabha Atom Res Ctr Mumbai Maharashtra India Bhabha Atom Res Ctr Astrophys Sci Div Mumbai Maharashtra India

ISBN: (纸本)9798350383782;9798350383799

weather parameters and weather forecasting play a crucial role in scientific experiments for the safe operation of the system. weather prediction involves high-performance computing, which enables building of weather models. Major Atmospheric Cherenkov Experiment (MACE) is a 21m ground-based, very high-energy gamma-ray telescope installed at Hanle, India. Accurate and reliable short-term weather prediction plays a crucial role in ensuring the safe and efficient functioning of the telescope. In the first part of the paper, we describe the multithreaded design of the real-time weather data capture application for the telescope. In the second part, we compare the performance of time series algorithms for weather prediction and the possibility of hybrid models for predicting non-stationary weather factors.

关键词： Gamma Ray Experiment weather data analysis Time Series Algorithm Computation

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Impact of the COVID-19 Pandemic on the 2020 Diurnal Temperature Range (DTR) in the Contiguous USA

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ATMOSPHERE 2022年第12期13卷 2031-2031页

作者： Ahmed, Walid Hoffmann, Lydia Marini Al-Hasani, Talib Santos, Rafael M. Univ Guelph Sch Engn 50 Stone Rd East Guelph ON N1G 2W1 Canada

Following the emergence of COVID-19, nations around the world implemented effective restrictions that limited people's movements and economic activity, which reportedly led to environmental improvements. The lowering of air emissions is one environmental indicator that has been connected to the pandemic. The diurnal temperature range (DTR) is one environmental indicator that has been linked to air pollution. In this study, it was hypothesized that because of the pandemic restrictions and slowdowns, the DTR in 2020 for a country that implemented major restrictive measures in reaction to the pandemic would be higher than in previous years, despite or in addition to background climatic forcings. Based on information from weather stations in the contiguous United States of America (USA), the DTR for the year 2020 was compared to the five years before it as a test of this hypothesis. It was verified that the annual mean DTR of 2020 was higher than the three years prior (2017-2019), but lower than the DTR of 2015 and 2016. Compared to historical trends (since 1911), the DTR change in 2020 is within past mean DTR variations that occurred over approx. 12-year cycles, linked to sunspot activity (Schwabe solar cycle). Moreover, climatic effects such as El Nino, La Nina and the prolonged trend of global warming reduce the confidence in the perceived effect of the pandemic. To determine if or how anthropogenic and environmental factors can magnify the impact of the COVID-19 restrictions on the regional mean DTR, five other parameters (annual snowfall quantities, gross domestic product per capita, population density, latitude (northern/southern), and longitude (coastal/inner)) were also examined against changes in DTR from 2015 to 2020. This analysis pointed to the environmental and industrial factors being more strongly correlated with short-term climate changes than societal factors and geographical location.

关键词： climate change anthropogenic impacts radiative forcing atmospheric albedo weather data analysis

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Classification of weather Conditions Based on Supervised Learning for Swedish Cities

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ATMOSPHERE 2023年第7期14卷 1174-1174页

作者： Safia, Mohamad Abbas, Rodi Aslani, Mohammad Univ Gavle Dept Comp & Geospatial Sci S-80176 Gavle Sweden

weather forecasting has always been challenging due to the atmosphere's complex and dynamic nature. weather conditions such as rain, clouds, clear skies, and sunniness are influenced by several factors, including temperature, pressure, humidity, wind speed, and direction. Physical and complex models are currently used to determine weather conditions, but they have their limitations, particularly in terms of computing time. In recent years, supervised machine learning methods have shown great potential in predicting weather events accurately. These methods use historical weather data to train a model, which can then be used to predict future weather conditions. This study enhances weather forecasting by employing four supervised machine learning techniques-artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and k-nearest neighbors (KNN)-on three distinct datasets obtained from the weatherstack database. These datasets, with varying temporal spans and uncertainty levels in their input features, are used to train and evaluate the methods. The results show that the ANN has superior performance across all datasets. Furthermore, when compared to weatherstack's weather prediction model, all methods demonstrate significant improvements. Interestingly, our models show variance in performance across different datasets, particularly those with predicted rather than observed input features, underscoring the complexities of handling data uncertainty. The study provides valuable insights into the use of supervised machine learning techniques for weather forecasting and contributes to the development of more precise prediction models.

关键词： cross-validation weather data analysis model training model evaluation support vector machines artificial neural networks k-nearest neighbors random forest machine learning weather prediction

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