Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of suppo...
With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of de...
Energy system models for the analysis of future scenarios are mainly driven by the set of energy service demands that define the broad outlines of socio-economic development throughout the model time horizon. Here, the long-term effects of the COVID-...
To predict the variability of dam water levels, parametric Multivariate Linear Regression (MLR), stochastic Vector AutoRegressive (VAR), Random Forest Regression (RFR) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) models were compar...
This study analyzes the economic system dynamics of investment in real estate from mainly four participants in China. Local governments limit the supply of commercial and residential land to raise fiscal revenue, and expand debts by land mortgage to...
One of the key indicators of a population’s well-being and the economic development of a country is represented by health, the main proxy for which is life expectancy at birth. Some factors, such as industrialization and modernization, have all...
In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rai...
In this manuscript, we analyze a bivariate vector auto-regressive (VAR) model in order to draw the design principle of a timeseries with a controlled statistical inter-relationship. We show how to generate bivariate timeseries with given covariance a...
This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural net...
This paper adopts the vector auto-regression model (VAR) to study the dynamic effect of renewable energy consumption on carbon dioxide emissions. Our model is based on a given level of primary energy consumption, economic growth and natural gas consu...
3 August 2022
As one of the three major engines of economic growth in China, the Beijing–Tianjin–Hebei (BTH) urban agglomeration has become one of the regions with the highest energy consumption intensity. To investigate the dynamic relationships betwe...
The motivation behind the study is continuous fluctuations in energy prices in Pakistan, so this study aims to investigate the role of a twin deficit, urbanization, climate change, energy production from oil and gas, and the exchange rate in energy i...
With the deepening of economic globalization, research and development (R&D) activities have become an important link in promoting the optimization of industrial structure and high-quality economic growth. To clarify the interaction between R&...
Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has b...
Turkey is increasingly concerned about the effects of climate change, weather unpredictability, and severe events on agricultural production, food loss, and livelihoods. Turkey has long struggled against climate variability and catastrophic climatic...
In order to capture an eye image of high quality in a gaze-tracking camera, an auto-focusing mechanism is used, which requires accurate focus assessment. Although there has been previous research on focus assessment in the spatial or wavelet domains,...
This study clarifies the impact of global supply chain risks on global logistics companies, with a focus on the potential implications for sustainable supply chain management. The study employs the vector auto-regression model to examine the relation...
This study endeavors to explore the shock-transmission mechanism between Trade Policy Uncertainty (TPU) and the volatility inherent in the Gulf Cooperation Council (GCC) Islamic stock markets by employing the novel Quantile Vector Auto Regression (QV...
This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditio...
Network vector AutoRegressive models play a vital role in multivariate time series analysis. However, previous research in the classic Network vector AutoRegressive (NAR) model is limited to strict assumptions of linearity and time-invariance of node...
High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the cr...
The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supp...
A digital economy parallel with green development holds profound significance for achieving sustainability. The primary objective of this study was to explore the synergistic interaction effects between the digital economy and green development in Ch...
14 July 2011
We have investigated the interactions between economic growth and industrial wastewater discharge from 1978 to 2007 in China's Hunan Province using co-integration theory and an error-correction model. Two main economic growth indicators and four repr...
In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artifici...
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of suppo...
A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or...
We analyze the dynamic interaction of Japan’s total factor productivity, gross domestic product (GDP) domestic and foreign private and public research and development (R&D) in vector-error-correction models (VECMs) for Japan with data from...
In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analys...
The main obstacle against the penetration of wind power into the power grid is its high variability in terms of wind speed fluctuations. Accurate power forecasting, while making maintenance more efficient, leads to the profit maximisation of power tr...
Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Mo...
Using time-series data from January 2006 to February 2021, this study analyzed the effect of macroeconomic shocks on the shipping and shipbuilding industries. The Granger causality test, recursive structural vector autoregressive models, impulse resp...
The problem of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to assess, theoretically and e...
The results of the single-equation cointegration tests indicate that patterns of cointegration in the two main and four sub-periods are not homogeneous. Two key findings emerge from the study. First, fewer stock markets cointegrated with S&P 500...
In urban basins, localized torrential rain increases the water level of rivers in an extremely short time, thereby leading to flooding within an hour. Therefore, to achieve early evacuation, the water level should be accurately forecasted. The outflo...
Electrocardiography (ECG) is a simple and safe tool for detecting heart conditions. Despite the diaspora of existing heartbeat classifiers, improvements such as real-time heartbeat identification and patient-independent classification persist. Reserv...
Pakistan is one of the largest recipients of remittances globally and has substantial remittance inflow fluctuations; thus, finding the remittance–gross domestic product (GDP) ratio threshold is expedient. This study examined the macroeconomic...
Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic mode...
In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as...
Machine learning has attracted much attention in the field of genomic prediction due to its powerful predictive capabilities, yet the lack of an explanatory nature in modeling decisions remains a major challenge. In this study, we propose a novel mac...
The sound and sustainable development of the international monetary system is the cornerstone of the sound and stable development of the global economy. This paper takes digital currency in China as its research object and utilizes a regime-switching...
This paper discusses the notion of cointegrating space for linear processes integrated of any order. It first shows that the notions of (polynomial) cointegrating vectors and of root functions coincide. Second, it discusses how the cointegrating spac...
In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and ut...
The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generate...
In response to questions about the relative importance of different types of capital flow for international competitiveness, we develop a structural vector auto-regressive model of the real exchange rate and international capital flows. We reveal tha...
Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price...
Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to i...
Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are oft...
Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing...
The global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective dis...
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