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

10.36227/techrxiv.13296047.v2 ◽

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>

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

10.36227/techrxiv.13296047.v1 ◽

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>

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Approaching Freshet beneath Landfast Ice in Kugmallit Bay on the Canadian Arctic Shelf: Evidence from Sensor and Ground Truth Data

ARCTIC ◽

10.14430/arctic8 ◽

2009 ◽

Vol 61 (1) ◽

pp. 76 ◽

Cited By ~ 6

Author(s):

Tony R. Walker ◽

Jon Grant ◽

Peter Jarvis

Keyword(s):

Arctic Ocean ◽

Ground Truth ◽

Open Water ◽

Early Spring ◽

The Arctic ◽

Ground Truth Data ◽

The North ◽

Landfast Ice ◽

North American Arctic ◽

Mackenzie River

The Mackenzie River is the largest river in the North American Arctic. Its huge freshwater and sediment load impacts the Canadian Beaufort Shelf, transporting large quantities of sediment and associated organic carbon into the Arctic Ocean. The majority of this sediment transport occurs during the freshet peak flow season (May to June). Mackenzie River-Arctic Ocean coupling has been widely studied during open water seasons, but has rarely been investigated in shallow water under landfast ice in Kugmallit Bay with field-based surveys, except for those using remote sensing. We observed and measured sedimentation rates (51 g m-2 d-1) and the concentrations of chlorophyll a (mean 2.2 ?g L-1) and suspended particulate matter (8.5 mg L-1) and determined the sediment characteristics during early spring, before the breakup of landfast ice in Kugmallit Bay. We then compared these results with comparable data collected from the same site the previous summer. Comparison of organic quality in seston and trapped material demonstrated substantial seasonal differences. The subtle changes in biological and oceanographic variables beneath landfast ice that we measured using sensors and field sampling techniques suggest the onset of a spring melt occurring hundreds of kilometres farther south in the Mackenzie Basin.

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Insights into the Short-Term Tidal Variability of Multibeam Backscatter from Field Experiments on Different Seafloor Types

Geosciences ◽

10.3390/geosciences9010034 ◽

2019 ◽

Vol 9 (1) ◽

pp. 34 ◽

Cited By ~ 9

Author(s):

Giacomo Montereale-Gavazzi ◽

Marc Roche ◽

Koen Degrendele ◽

Xavier Lurton ◽

Nathan Terseleer ◽

...

Keyword(s):

High Frequency ◽

Field Experiments ◽

Ground Truth ◽

Environmental Data ◽

Absolute Calibration ◽

Short Term ◽

Ground Truth Data ◽

Nearshore Area ◽

The North ◽

Sediment Interface

Three experiments were conducted in the Belgian part of the North Sea to investigate short-term variation in seafloor backscatter strength (BS) obtained with multibeam echosounders (MBES). Measurements were acquired on predominantly gravelly (offshore) and sandy and muddy (nearshore) areas. Kongsberg EM3002 and EM2040 dual MBES were used to carry out repeated 300-kHz backscatter measurements over tidal cycles (~13 h). Measurements were analysed in complement to an array of ground-truth variables on sediment and current nature and dynamics. Seafloor and water-column sampling was used, as well as benthic landers equipped with different oceanographic sensors. Both angular response (AR) and mosaicked BS were derived. Results point at the high stability of the seafloor BS in the gravelly area (<0.5 dB variability at 45° incidence) and significant variability in the sandy and muddy areas with envelopes of variability >2 dB and 4 dB at 45° respectively. The high-frequency backscatter sensitivity and short-term variability are interpreted and discussed in the light of the available ground-truth data for the three experiments. The envelopes of variability differed considerably between areas and were driven either by external sources (not related to the seafloor sediment), or by intrinsic seafloor properties (typically for dynamic nearshore areas) or by a combination of both. More specifically, within the gravelly areas with a clear water mass, seafloor BS measurements where unambiguous and related directly to the water-sediment interface. Within the sandy nearshore area, the BS was shown to be strongly affected by roughness polarization processes, particularly due to along- and cross-shore current dynamics, which were responsible for the geometric reorganization of the morpho-sedimentary features. In the muddy nearshore area, the BS fluctuation was jointly driven by high-concentrated mud suspension dynamics, together with surficial substrate changes, as well as by water turbidity, increasing the transmission losses. Altogether, this shows that end-users and surveyors need to consider the complexity of the environment since its dynamics may have severe repercussions on the interpretation of BS maps and change-detection applications. Furthermore, the experimental observations revealed the sensitivity of high-frequency BS values to an array of specific configurations of the natural water-sediment interface which are of interest for monitoring applications elsewhere. This encourages the routine acquisition of different and concurrent environmental data together with MBES survey data. In view of promising advances in MBES absolute calibration allowing more straightforward data comparison, further investigations of the drivers of BS variability and sensitivity are required.

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Disambiguating Energy Disaggregation: A Collective Probabilistic Approach

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/398 ◽

2017 ◽

Cited By ~ 4

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%.

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SINTEF/IKU OIL-WEATHERING MODEL: PREDICTING OILS' PROPERTIES AT SEA

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-1997-1-297 ◽

1997 ◽

Vol 1997 (1) ◽

pp. 297-307 ◽

Cited By ~ 12

Author(s):

Per S. Daling ◽

Ole Morten Aamo ◽

Alun Lewis ◽

Tove Strøm-Kristiansen

Keyword(s):

Oil Spill ◽

Oil Spills ◽

Ground Truth ◽

Field Trials ◽

Ground Truth Data ◽

The North ◽

Offshore Installations ◽

The North Sea ◽

Good Laboratory ◽

Basic Input

ABSTRACT This paper describes the empirical approach used in the development of the IKU oil-weathering model (IKU-OWM). Weathering data from field trials with experimental oil spills during the past 15 years have provided the basis for the algorithms used in the model. These data, combined with a standardized laboratory study of each specific oil, including a bench-scale (“stepwise”) weathering investigation, a chemical dispersibility investigation, and a meso-scale flume basin weathering investigation, form the basic input to the model. The IKU-OWM has been extensively tested and verified with results from full-scale field trials with experimental oil slicks in the North Sea in 1994 and 1995. This paper presents the correlations between the oil-weathering values predicted by the model and the ground-truth data obtained from the field trials. To obtain reliable weathering predictions, it is essential to have good laboratory weathering data of specific oils. The IKU-OWM is now extensively used in Norway to predict the changes in oil properties due to weathering under user-specified conditions. Predictions provided by the IKU-OWM enable oil spill personnel to estimate the most appropriate “window of opportunity” for using chemical dispersants in various spill situations. Prespill scenario analysis with the IKU's oil spill contingency and response (OSCAR) model system, of which the IKU-OWM is one of several components, has become an important part of contingency plans and of contingency training of oil spill personnel at refineries, oil terminals, and offshore installations in Norway.

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Measuring Ocean Bottom Pressure at the North Pole

Marine Technology Society Journal ◽

10.4031/mtsj.48.5.11 ◽

2014 ◽

Vol 48 (5) ◽

pp. 52-68 ◽

Cited By ~ 2

Author(s):

Cecilia Peralta-Ferriz ◽

James H. Morison ◽

Scott E. Stalin ◽

Christian Meinig

Keyword(s):

Arctic Ocean ◽

Ground Truth ◽

Satellite System ◽

North Pole ◽

Ocean Bottom ◽

Bottom Pressure ◽

Ground Truth Data ◽

Ocean Bottom Pressure ◽

The North ◽

Aircraft Landing

AbstractHigh-precision deep Arctic Bottom Pressure Recorders (ABPRs) were developed to measure ocean bottom pressure variations in the perennial ice-covered Arctic Ocean. The ABPRs use the tsunami detection DART acoustic modem technology and have been programmed to store and transmit the data acoustically without the need to recover the instrument. ABPRs have been deployed near the North Pole, where the ice cover is a year-round challenge for access with a ship. Instead, the ABPRs have been built as light-weight mechanical systems that we can install using aircraft landing on the ice. ABPRs have provided the first records of uninterrupted pressure data over continuous years ever made in the central Arctic. The ABPR data have allowed us to identify and understand modes of Arctic Ocean bottom pressure variability that were unknown before the ABPR records and have offered new means of investigating and understanding the rapidly changing Arctic system. The ABPR records have also shown outstanding agreement with the satellite-sensed ocean bottom pressure anomalies from GRACE, providing ground truth data for validation of the satellite system. Due to the successful science findings as well as the ABPRs' capability to fulfill the upcoming potential gaps of pressure measurements between GRACE and a GRACE follow-on mission, we highlight the urgent need to develop and maintain an Arctic observing network using ABPRs.

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Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression

Remote Sensing ◽

10.3390/rs12040665 ◽

2020 ◽

Vol 12 (4) ◽

pp. 665 ◽

Cited By ~ 3

Author(s):

Ricardo M. Llamas ◽

Mario Guevara ◽

Danny Rorabaugh ◽

Michela Taufer ◽

Rodrigo Vargas

Keyword(s):

Soil Moisture ◽

Spatial Data ◽

Linear Models ◽

European Space Agency ◽

Ground Truth ◽

Frozen Soil ◽

Ground Truth Data ◽

Model Soil ◽

The North ◽

The Usa

Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., European Space Agency Climate Change Initiative (ESA CCI), National Aeronautics and Space Administration Soil Moisture Active Passive (NASA SMAP)); however, there are regions where satellite-derived soil moisture cannot be estimated because of certain conditions such as high canopy density, frozen soil, or extremely dry soil. We compared and tested three approaches, ordinary kriging (OK), regression kriging (RK), and generalized linear models (GLMs), to model soil moisture and fill spatial data gaps from the ESA CCI product version 4.5 from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation (r = 0.558, RMSE = 0.069 m3m−3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation with NASMD data when using 100% (r = 0.579, RMSE = 0.067 m3m−3), 75% (r = 0.575, RMSE = 0.067 m3m−3), and 50% (r = 0.569, RMSE = 0.067 m3m−3) of available valid pixels for each month of the study period. RK showed comparable values to OK when using different percentages of available valid pixels, 100% (r = 0.582, RMSE = 0.067 m3m−3), 75% (r = 0.582, RMSE = 0.067 m3m−3), and 50% (r = 0.571, RMSE = 0.067 m3m−3). GLM had slightly lower correlation with NASMD data (average r = 0.475, RMSE = 0.070 m3m−3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for using geostatistical approaches (OK and RK) as alternative techniques to gap-fill missing spatial values of satellite-derived soil moisture.

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