Salt is the Seed of Life: a Geotourism Potential Analysis of Salt Areas in Buzău Land, Romania

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

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Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽

10.3390/en14133800 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3800

Author(s):

Sebastian Krapf ◽

Nils Kemmerzell ◽

Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽

Manuel Hack Hack Vázquez ◽

Fabian Netzler ◽

...

Keyword(s):

Deep Learning ◽

System Analysis ◽

State Of The Art ◽

Critical Role ◽

Semantic Segmentation ◽

Energy System ◽

Aerial Images ◽

Potential Analysis ◽

3D Data ◽

Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

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A Behavioral and Electrophysiological Investigation of Effects of Visual Congruence on Olfactory Sensitivity During Habituation to Prolonged Odors

Chemical Senses ◽

10.1093/chemse/bjaa065 ◽

2020 ◽

Vol 45 (9) ◽

pp. 845-854

Author(s):

Nicholas Fallon ◽

Timo Giesbrecht ◽

Anna Thomas ◽

Andrej Stancak

Keyword(s):

Cognitive Processing ◽

Visual Cues ◽

Reaction Times ◽

Event Related Potential ◽

Detection Sensitivity ◽

Moderate Intensity ◽

Odor Perception ◽

Potential Analysis ◽

Odor Detection ◽

Central Processing

Abstract Congruent visual cues augment sensitivity to brief olfactory presentations and habituation of odor perception is modulated by central-cognitive processing including context. However, it is not known whether habituation to odors could interact with cross-modal congruent stimuli. The present research investigated the effect of visual congruence on odor detection sensitivity during continuous odor exposures. We utilized a multimethod approach, including subjective behavioral responses and reaction times (RTs; study 1) and electroencephalography (EEG, study 2). Study 1: 25 participants received 2-min presentations of moderate-intensity floral odor delivered via olfactometer with congruent (flower) and incongruent (object) image presentations. Participants indicated odor perception after each image. Detection sensitivity and RTs were analyzed in epochs covering the period of habituation. Study 2: 25 new participants underwent EEG recordings during 145-s blocks of odor presentations with congruent or incongruent images. Participants passively observed images and intermittently rated the perceived intensity of odor. Event-related potential analysis was utilized to evaluate brain processing related to odor–visual pairs across the period of habituation. Odor detection sensitivity and RTs were improved by congruent visual cues. Results highlighted a diminishing influence of visual congruence on odor detection sensitivity as habituation occurred. Event-related potential analysis revealed an effect of congruency on electrophysiological processing in the N400 component. This was only evident in early periods of odor exposure when perception was strong. For the first time, this demonstrates the modulation of central processing of odor–visual pairs by habituation. Frontal negativity (N400) responses encode the aspects of cross-modal congruence for odor–vision cross-modal tasks.

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