Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

2020 ◽  
Author(s):  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Energy Demand ◽  
Heat Load ◽  
Demand Estimation ◽  
Load Detection ◽  
The Impact

Impact of Rooftop PV Shading on Net Electrical Energy Demand of Buildings in Pakistan

2021 ◽  
Vol 3 (1) ◽  
pp. 45-49
Author(s):  
Muhammad Umar Maqbool ◽  
Arslan Dawood Butt ◽  
Abdul Rauf Bhatti ◽  
Yawar Ali Sheikh ◽  
Muhammad Waleed Asif
Keyword(s):  
Energy Demand ◽  
Heat Load ◽  
Layer Structure ◽  
Electrical Energy ◽  
Position Angle ◽  
Net Energy ◽  
Ambient Conditions ◽  
Pv System ◽  
Roof Structure ◽  
The Impact

This work performs a quantitative assessment of the impact of rooftop PV installation on building’s net energy demand using model of roof structure and steady state thermal simulations. For this purpose, roof structure typically used in Faisalabad, Pakistan is modeled with and without the shading effect due to a 395 W commercial rooftop PV setup. The simulated parameters include the impact of PV module’s dimensions, mounting position/angle alongside roof size and ambient conditions on heat load of air-conditioning system to maintain a temperature of 25 °C within building’s top floor. During the daylight hours of July, the heat load added by the roof on average reduces from 150.87 BTU/h/m2 without PV to 118.16 BTU/h/m2 with PV structure. This 20.05% reduction in energy demand has been achieved with July’s maximum daytime solar and infrared irradiances of 792.2 W/m2 and 466 W/m2 recorded at an average ambient temperature of 35.5 °C and wind speed of 2.75 m/s. This study provides valuable data on optimization of roof layer structure during building’s construction in anticipation of PV system installation at a later stage. Also developed techniques/methods to reduce building’s energy budget due to PV installation, can be valuable input for construction industry as well.

The impact of deep learning–based equipment usage detection on building energy demand estimation

2021 ◽  
pp. 014362442110347
Author(s):  
Shuangyu Wei ◽  
Paige Wenbin Tien ◽  
Yupeng Wu ◽  
John Kaiser Calautit
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Energy Demand ◽  
Internal Heat ◽  
Building Energy ◽  
Detection Accuracy ◽  
Equipment Usage ◽  
The Impact ◽  
Detection And Recognition

As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions. Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

Predictive Analysis of the Impact of Corporate R&D Support Using Deep Learning

2020 ◽  
Vol 23 (1) ◽  
pp. 20-41
Author(s):  
Pilseong Jang ◽  
Jaeyoun You ◽  
Seung Hwan Oh
Keyword(s):  
Deep Learning ◽  
Predictive Analysis ◽  
The Impact

scholarly journals The impact of deep learning on document classification using semantically rich representations

2019 ◽  
Vol 56 (5) ◽  
pp. 1618-1632 ◽  
Author(s):  
Zenun Kastrati ◽  
Ali Shariq Imran ◽  
Sule Yildirim Yayilgan
Keyword(s):  
Deep Learning ◽  
Document Classification ◽  
The Impact

scholarly journals Case study of a water bioengineering construction site in Austria. Ecological aspects and application of an environmental life cycle assessment model

2021 ◽  
Author(s):  
M. von der Thannen ◽  
S. Hoerbinger ◽  
C. Muellebner ◽  
H. Biber ◽  
H. P. Rauch
Keyword(s):  
Global Warming ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Demand ◽  
Assessment Model ◽  
Construction Site ◽  
Negative Effects ◽  
Environmental Life Cycle Assessment ◽  
The Impact ◽  
Soil And Water

AbstractRecently, applications of soil and water bioengineering constructions using living plants and supplementary materials have become increasingly popular. Besides technical effects, soil and water bioengineering has the advantage of additionally taking into consideration ecological values and the values of landscape aesthetics. When implementing soil and water bioengineering structures, suitable plants must be selected, and the structures must be given a dimension taking into account potential impact loads. A consideration of energy flows and the potential negative impact of construction in terms of energy and greenhouse gas balance has been neglected until now. The current study closes this gap of knowledge by introducing a method for detecting the possible negative effects of installing soil and water bioengineering measures. For this purpose, an environmental life cycle assessment model has been applied. The impact categories global warming potential and cumulative energy demand are used in this paper to describe the type of impacts which a bioengineering construction site causes. Additionally, the water bioengineering measure is contrasted with a conventional civil engineering structure. The results determine that the bioengineering alternative performs slightly better, in terms of energy demand and global warming potential, than the conventional measure. The most relevant factor is shown to be the impact of the running machines at the water bioengineering construction site. Finally, an integral ecological assessment model for applications of soil and water bioengineering structures should point out the potential negative effects caused during installation and, furthermore, integrate the assessment of potential positive effects due to the development of living plants in the use stage of the structures.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Impact of Passive Energy Efficiency Measures on Cooling Energy Demand in an Architectural Campus Building in Karachi, Pakistan

2021 ◽  
Vol 13 (13) ◽  
pp. 7251
Author(s):  
Mushk Bughio ◽  
Muhammad Shoaib Khan ◽  
Waqas Ahmed Mahar ◽  
Thorsten Schuetze
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Information Modeling ◽  
Base Case ◽  
Energy Efficiency Measures ◽  
Efficiency Measures ◽  
The Impact ◽  
Cooling Energy Demand ◽  
Campus Building

Electric appliances for cooling and lighting are responsible for most of the increase in electricity consumption in Karachi, Pakistan. This study aims to investigate the impact of passive energy efficiency measures (PEEMs) on the potential reduction of indoor temperature and cooling energy demand of an architectural campus building (ACB) in Karachi, Pakistan. PEEMs focus on the building envelope’s design and construction, which is a key factor of influence on a building’s cooling energy demand. The existing architectural campus building was modeled using the building information modeling (BIM) software Autodesk Revit. Data related to the electricity consumption for cooling, building masses, occupancy conditions, utility bills, energy use intensity, as well as space types, were collected and analyzed to develop a virtual ACB model. The utility bill data were used to calibrate the DesignBuilder and EnergyPlus base case models of the existing ACB. The cooling energy demand was compared with different alternative building envelope compositions applied as PEEMs in the renovation of the existing exemplary ACB. Finally, cooling energy demand reduction potentials and the related potential electricity demand savings were determined. The quantification of the cooling energy demand facilitates the definition of the building’s electricity consumption benchmarks for cooling with specific technologies.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

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