scholarly journals Disambiguating Energy Disaggregation: A Collective Probabilistic Approach

2017 ◽  
Author(s):  
Sabina Tomkins ◽  
Jay Pujara ◽  
Lise Getoor
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Negative Impact ◽  
Probabilistic Approach ◽  
Ground Truth ◽  
Solution Quality ◽  
Ground Truth Data ◽  
Hinge Loss ◽  
Energy Disaggregation ◽  
Real World Datasets

Reducing household energy usage is a priority for improving the resiliency and stability of the power grid and decreasing the negative impact of energy consumption on the environment and public health.Relevant and timely feedback about the power consumption of specific appliances can help household residents to reduce their energy demand. Given only a total energy reading, such as that collected from a residential meter, energy disaggregation strives to discover the consumption of individual appliances. Existing disaggregation algorithms are computationally inefficient and rely heavily on high-resolution ground truth data. We introduce a probabilistic framework which infers the energy consumption of individual appliances using a hinge-loss Markov random field (HL-MRF), which admits highly scalable inference. To further enhance efficiency, we introduce a temporal representation which leverages state duration. We also explore how contextual information impacts solution quality with low-resolution data. Our framework is flexible in its ability to incorporate additional constraints; by constraining appliance usage with context and duration we can better disambiguate appliances with similar energy consumption profiles. We demonstrate the effectiveness of our framework on two public real-world datasets, reducing the error relative to a previous state-of-the-art method by as much as 50%.

In Search of Causal Relationship between FDI, GDP, and Energy Consumption - Evidence from China

2012 ◽  
Vol 524-527 ◽  
pp. 3388-3391 ◽  
Author(s):  
Kuo Cheng Kuo ◽  
Chi Ya Chang ◽  
Mei Hui Chen ◽  
Wei Yu Chen
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Environmental Protection ◽  
Granger Causality ◽  
Energy Demand ◽  
Negative Impact ◽  
Demand Management ◽  
Effective Control ◽  
Chinese Government ◽  
Negative Effects

The balance between economic growth and environmental protection has been the core concern of policy makers in developing countries for the past two decades. This study is one of the few studies to empirically inspect the relationship between economic growth, FDI, and energy consumption over the period 1978-2010 in China. The results reveal that there is a unidirectional Granger causality running from GDP to energy consumption. This suggests that increase of GDP will consume more energy and implementing of the energy conservation policies and energy demand management policies in China may not have negative impact on economic growth. Besides, a bi-directional Granger causality has been found between energy consumption and FDI. This implies that Chinese government should cautiously evaluate the positive and negative effects of FDI inflows and put efforts into making more effective control policies on environmental protection.

scholarly journals The Effect of Scale, Technique, Composition and Trade Openness on Energy Demand: Fresh Evidence from Malaysia

2019 ◽  
Author(s):  
Muhammad Shahbaz ◽  
Md. Mahmudul Alam ◽  
Gazi Salah Uddin ◽  
Loganathan Nanthakumar
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Structural Breaks ◽  
Negative Impact ◽  
Trade Openness ◽  
Growth Effect ◽  
Demand Model ◽  
Bounds Testing ◽  
Long Run ◽  
The Impact

The aim of this paper utilizes an energy demand model to investigate the impact of trade openness on energy consumption by incorporating scale and technique, composition and urbanization effects in the case of Malaysia. The study covers the sample period of 1970-2011 using quarter frequency data. We applied the bounds testing approach in the presence of structural breaks to examine the long run relationship between the variables. The VECM Granger causality is used to detect the direction of causality between the variables. Our findings indicate that growth effect (scale and technique effect) has a positive (negative) impact on energy consumption whereas composition effect stimulates energy demand in Malaysia.. Energy consumption is positively influenced by both from openness and urbanization. This study opens new policy insights for policy making authorities to articulate a comprehensive energy and trade policy to sustain economic growth and improve the environmental quality of Malaysia.

scholarly journals Assessment of magnetic data for landfill characterization by means of a probabilistic approach

2020 ◽  
Author(s):  
Itzel Isunza Manrique ◽  
David Caterina ◽  
Cornelia Inauen ◽  
Arnaud Watlet ◽  
Ben Dashwood ◽  
...  
Keyword(s):  
Structural Model ◽  
Probabilistic Approach ◽  
Geophysical Methods ◽  
Ground Truth ◽  
Spectral Ratio ◽  
Disposal Site ◽  
Magnetic Data ◽  
Ground Truth Data ◽  
Vertical Extension ◽  
Magnetic Modelling

<p>The sustainable vision of the Dynamic Landfill Management (DLM) deals not only with present but also with long-term waste management. In this context, DLM enhances the environmental assessment of landfills after closure as well as the recovery of materials and energy resources, for which, a proper characterization is required. To this end, geophysical methods have demonstrated their suitability for landfill exploration, characterization and monitoring. Due to the complexity of these sites and challenges in data acquisition and/or processing, the use of multiple methods is the best approach for landfill investigations. In this work, we used multiple geophysical methods, co-located with several trial pits and boreholes, to estimate the structure of a waste disposal site located in a quarry, and to better delineate the underlying geology composed of limestone. We applied electrical resistivity tomography (ERT), time-domain induced polarization (IP), H/V spectral ratio from microtremor records and magnetometry. We made a structural joint interpretation using the different datasets and the ground truth data. First, the ERT and IP data were individually inverted, and a first structural model was derived. Afterwards, we followed a parametric analysis of the H/V data to corroborate the thickness of some layers at the position of the seismic stations. Then, this model was used to compute synthetic magnetic data and by comparing them with the observed total field magnetic anomalies, a refined model was produced. We evaluated the improvement of including magnetic modelling by using a probabilistic approach previously reported. This approach is based on the computation of conditional probabilities by comparing the inverted models with the co-located data from trial pits and boreholes. Overall, we delineated the lateral and vertical extension of the waste body, the distribution of ash and lime deposits and estimated the upper limit structure of the bedrock.</p>

scholarly journals Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Massimo La Scala ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Energy Demand ◽  
Ground Truth ◽  
Annual Conference ◽  
Power Demand ◽  
Time Data ◽  
Feed Forward Neural Network ◽  
Ground Truth Data ◽  
The North ◽  
Economic Framework ◽  
Economic Trends

<div>The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility.</div><div><br></div><div>Preprint workd. Preliminary work of the accepted paper in 2020 AEIT International Annual Conference (AEIT). <br></div><div>DOI: https://doi.org/10.23919/AEIT50178.2020.9241136<br></div>

scholarly journals Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Massimo La Scala ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Energy Demand ◽  
Ground Truth ◽  
Annual Conference ◽  
Power Demand ◽  
Time Data ◽  
Feed Forward Neural Network ◽  
Ground Truth Data ◽  
The North ◽  
Economic Framework ◽  
Economic Trends

<div>The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility.</div><div><br></div><div>Preprint workd. Preliminary work of the accepted paper in 2020 AEIT International Annual Conference (AEIT). <br></div><div>DOI: https://doi.org/10.23919/AEIT50178.2020.9241136<br></div>

scholarly journals Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy

2020 ◽  
Author(s):  
Paolo Scarabaggio
Keyword(s):  
Energy Demand ◽  
Ground Truth ◽  
Annual Conference ◽  
Power Demand ◽  
Time Data ◽  
Feed Forward Neural Network ◽  
Ground Truth Data ◽  
The North ◽  
Economic Framework ◽  
Economic Trends

<div>The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility.</div><div><br></div><div>Preprint workd. Preliminary work of the accepted paper in 2020 AEIT International Annual Conference (AEIT). <br></div><div>DOI: https://doi.org/10.23919/AEIT50178.2020.9241136<br></div>

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals The Urbanisation Impacts on the Policy Effects of the Carbon Tax in China

2021 ◽  
Vol 13 (12) ◽  
pp. 6749
Author(s):  
Shuyang Chen
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Computable General Equilibrium ◽  
Carbon Tax ◽  
Negative Impact ◽  
Positive Impact ◽  
Evaluation Framework ◽  
Socioeconomic Impacts ◽  
Policy Effects ◽  
Distributed Lag

In the literature, very few studies have focused on how urbanisation will influence the policy effects of a climate policy even though urbanisation does have profound socioeconomic impacts. This paper has explored the interrelations among the urbanisation, carbon emissions, GDP, and energy consumption in China using the autoregressive distributed lag (ARDL) model. Then, the unit urbanisation impacts are inputted into the policy evaluation framework of the Computable General Equilibrium (CGE) model in 2015–2030. The results show that the urbanisation had a positive impact on the GDP but a negative impact on the carbon emissions in 1980–2014. These impacts were statistically significant, but its impact on the energy consumption was not statistically significant. In 2015–2030, the urbanisation will have negative impacts on the carbon emissions and intensity. It will decrease the GDP and the household welfare under the carbon tax. The urbanisation will increase the average social cost of carbon (ASCC). Hence, the urbanisation will reinforce the policy effects of the carbon tax on the emissions and welfare.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

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