scholarly journals Robust Graph Factorization for Multivariate Electricity Consumption Series Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kaihong Zheng ◽  
Honghao Liang ◽  
Lukun Zeng ◽  
Xiaowei Chen ◽  
Sheng Li ◽  
...  
Keyword(s):  
Multivariate Time Series ◽  
Electricity Consumption ◽  
Electricity Production ◽  
Series Data ◽  
The Past ◽  
Time Period ◽  
Factorization Model ◽  
Spectral Embedding ◽  
Similarity Graph ◽  
Graph Factorization

Multivariate electricity consumption series clustering can reflect trends of power consumption changes in the past time period, which can provide reliable guidance for electricity production. However, there are some abnormal series in the past multivariate electricity consumption series data, while outliers will affect the discovery of electricity consumption trends in different time periods. To address this problem, we propose a robust graph factorization model for multivariate electricity consumption clustering (RGF-MEC), which performs graph factorization and outlier discovery simultaneously. RGF-MEC first obtains a similarity graph by calculating distance among multivariate electricity consumption series data and then performs robust matrix factorization on the similarity graph. Meanwhile, the similarity graph is decomposed into a class-related embedding and a spectral embedding, where the class-related embedding directly reveals the final clustering results. Experimental results on realistic multivariate time-series datasets and multivariate electricity consumption series datasets demonstrate effectiveness of the proposed RGF-MEC model.

scholarly journals Time-Series Causality with Missing Data

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Bo Yuan Chang ◽  
Mohamed A. Naiel ◽  
Steven Wardell ◽  
Stan Kleinikkink ◽  
John S. Zelek
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Missing Values ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Causal Relationships ◽  
Sampled Data ◽  
The Past

Over the past years, researchers have proposed various methods to discover causal relationships among time-series data as well as algorithms to fill in missing entries in time-series data. Little to no work has been done in combining the two strategies for the purpose of learning causal relationships using unevenly sampled multivariate time-series data. In this paper, we examine how the causal parameters learnt from unevenly sampled data (with missing entries) deviates from the parameters learnt using the evenly sampled data (without missing entries). However, to obtain the causal relationship from a given time-series requires evenly sampled data, which suggests filling the missing data values before obtaining the causal parameters. Therefore, the proposed method is based on applying a Gaussian Process Regression (GPR) model for missing data recovery, followed by several pairwise Granger causality equations in Vector Autoregssive form to fit the recovered data and obtain the causal parameters. Experimental results show that the causal parameters generated by using GPR data filling offers much lower RMSE than the dummy model (fill with last seen entry) under all missing values percentage, suggesting that GPR data filling can better preserve the causal relationships when compared with dummy data filling, thus should be considered when dealing with unevenly sampled time-series causality learning.

scholarly journals A Hybrid Arima and Discrete Wavelet Transform Model for Predicting the Electricity Consumption of Punjab

2019 ◽  
Vol 8 (11) ◽  
pp. 1915-1919
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Arima Model ◽  
Electricity Consumption ◽  
Electricity Production ◽  
Series Data ◽  
Support Vector ◽  
Discrete Wavelet ◽  
World Population ◽  
Proposed Model

The significant increase in the world population increases the demand for energy which seems to be alarming for the electricity production boards in the existing time. In the last decade, there are various engineering, simulation tools, and artificial intelligence-based methods such as Support Vector Machine and Artificial Neural Network proposed in the literature to forecast the optimal electricity demand. But these models seldom to work with the linear data. In this paper, a reliable prediction model using the linear time series data of the previous years from January 2013 to December 2017 has been presented to forecast the electricity consumption in Punjab, India. Initially, Discrete Wavelet Transform (DWT) analysis presented to extract the upper and lower limit of the previous year dataand then AutoRegressive Integrated Moving Average (ARIMA) model has been applied to extract the forecast values. The experimental results compared the original and predicted value using the proposed model to evaluate the effectiveness of the proposed approach. The results show that the difference between the original and proposed modelis only 9% while that of ARIMA only it is 11%. Thus, the proposed model using ARIMA and DWT provides effective results in predicting the forecast value.

World Economy: Prospects and Barriers for Recovery

2012 ◽  
pp. 61-83 ◽  
Author(s):  
M. Ershov
Keyword(s):  
World Economy ◽  
Western World ◽  
Short Time Period ◽  
Market Segments ◽  
Junk Bonds ◽  
The Past ◽  
Time Period ◽  
The Us ◽  
Global Liquidity ◽  
Short Time

According to the latest forecasts, it will take 10 years for the world economy to get back to “decent shape”. Some more critical estimates suggest that the whole western world will have a “colossal mess” within the next 5–10 years. Regulators of some major countries significantly and over a short time‑period changed their forecasts for the worse which means that uncertainty in the outlook for the future persists. Indeed, the intensive anti‑crisis measures have reduced the severity of the past problems, however the problems themselves have not disappeared. Moreover, some of them have become more intense — the eurocrisis, excessive debts, global liquidity glut against the backdrop of its deficit in some of market segments. As was the case prior to the crisis, derivatives and high‑risk operations with “junk” bonds grow; budget problems — “fiscal cliff” in the US — and other problems worsen. All of the above forces the regulators to take unprecedented (in their scope and nature) steps. Will they be able to tackle the problems which emerge?

scholarly journals One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves

2021 ◽  
Vol 18 (10) ◽  
pp. 5104
Author(s):  
Iván Area ◽  
Henrique Lorenzo ◽  
Pedro J. Marcos ◽  
Juan J. Nieto
Keyword(s):  
Intensive Care Unit ◽  
Hospital Admissions ◽  
Age Groups ◽  
Age Group ◽  
Nw Spain ◽  
The Past ◽  
Global View ◽  
Time Period ◽  
Key Variables ◽  
One Year

In this work we look at the past in order to analyze four key variables after one year of the COVID-19 pandemic in Galicia (NW Spain): new infected, hospital admissions, intensive care unit admissions and deceased. The analysis is presented by age group, comparing at each stage the percentage of the corresponding group with its representation in the society. The time period analyzed covers 1 March 2020 to 1 April 2021, and includes the influence of the B.1.1.7 lineage of COVID-19 which in April 2021 was behind 90% of new cases in Galicia. It is numerically shown how the pandemic affects the age groups 80+, 70+ and 60+, and therefore we give information about how the vaccination process could be scheduled and hints at why the pandemic had different effects in different territories.

scholarly journals Metaphors of migration over time

2021 ◽  
pp. 095792652199215
Author(s):  
Charlotte Taylor
Keyword(s):  
Emigration And Immigration ◽  
Emigrants And Immigrants ◽  
Economic Resource ◽  
Historical Discourse ◽  
Continuity And Change ◽  
The Past ◽  
Perception Of Control ◽  
Time Period ◽  
Metaphor Use ◽  
The Uk

This paper aims to cast light on contemporary migration rhetoric by integrating historical discourse analysis. I focus on continuity and change in conventionalised metaphorical framings of emigration and immigration in the UK-based Times newspaper from 1800 to 2018. The findings show that some metaphors persist throughout the 200-year time period (liquid, object), some are more recent in conventionalised form (animals, invader, weight) while others dropped out of conventionalised use before returning (commodity, guest). Furthermore, we see that the spread of metaphor use goes beyond correlation with migrant naming choices with both emigrants and immigrants occupying similar metaphorical frames historically. However, the analysis also shows that continuity in metaphor use cannot be assumed to correspond to stasis in framing and evaluation as the liquid metaphor is shown to have been more favourable in the past. A dominant frame throughout the period is migrants as an economic resource and the evaluation is determined by the speaker’s perception of control of this resource.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals A Comparative Assessment for the Potential Energy Production from PV Installation on Residential Buildings

2020 ◽  
Vol 12 (24) ◽  
pp. 10344
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Mohammed Itma
Keyword(s):  
Potential Energy ◽  
Energy Production ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Residential Building ◽  
Electricity Production ◽  
Energy Sustainability ◽  
Pv Systems ◽  
Residential Units ◽  
Future Consumption

This paper targets the future energy sustainability and aims to estimate the potential energy production from installing photovoltaic (PV) systems on the rooftop of apartment’s residential buildings, which represent the largest building sector. Analysis of the residential building typologies was carried out to select the most used residential building types in terms of building roof area, number of floors, and the number of apartments on each floor. A computer simulation tool has been used to calculate the electricity production for each building type, for three different tilt angles to estimate the electricity production. Tilt angle, spacing between the arrays, the building shape, shading from PV arrays, and other roof elements were analyzed for optimum and maximum electricity production. The electricity production for each household has been compared to typical household electricity consumption and its future consumption in 2030. The results show that installing PV systems on residential buildings can speed the transition to renewable energy and energy sustainability. The electricity production for building types with 2–4 residential units can surplus their estimated future consumption. Building types with 4–8 residential units can produce their electricity consumption in 2030. Building types of 12–24 residential units can produce more than half of their 2030 future consumption.

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