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The purpose of this paper is to examine the impact of remittance inflows (remittances) on electricity consumption and electric power losses in Jamaica.

The authors use annual data from 1976 to 2014 and apply vector error correction modelling, Granger causality testing and impulse response analysis.

First, the authors find that there is co-integration between remittances and the energy variables, namely electricity consumption and electric power losses. Second, short-run Granger causality exists between the energy variables and remittances. This causality is bidirectional between the energy variables and positive changes in remittances, but it is unidirectional running from the energy variables to negative movements in remittances. Third, the authors find that in the long-run remittances have a negative relationship with electric power losses and a positive relationship with the consumption of electricity.

Findings from this paper will help to elucidate the relationship between electricity consumption, and electric power losses, and remittances.

The problem of electric power losses is acute in Jamaica and it is mostly due to theft. At the same time, Jamaica receives significant remittances. Social policy could have a role to encourage the use of remittances to help stem the theft of electricity.

This is the first study that examines the relationships between remittances, electricity consumption and electric power losses.

The purpose of this paper is to find potential causality and comparative relationships between electric power consumption, foreign direct investment and economic growth for India and Pakistan.

Granger causality tests have been employed for estimating the short and long run relationships between the variables, along with the adoption of co‐integration and error correction mechanism.

Empirical evidence for India covering a period of 1975‐2008 indicates long run causalities for electric power consumption and foreign direct investment boosting economic growth, electric power consumption and economic growth impacting foreign direct investment. For Pakistan, causality was established for foreign direct investment and economic growth inducing electric power consumption in the long run.

For India, there is a strong need of policy that would guarantee secure and continued supply of electricity, as enhanced electric consumption is expected to boost foreign direct investment and economic growth. Pakistan should aim for cost‐effective, stable and environment friendly alternate to fossil fuels as the main source of its electric power generation.

Literature on the electricity consumption‐FDI‐economic growth nexus is scarce. The present study adds to this strand of literature. Also for the first time, in this scenario, this paper uses two economies (India and Pakistan), provides a comparative analysis of the empirical results and presents prospective explanations for the observed causality differences between the two economies.

To discuss integration of the rapid prototyping environmental aspects with the primary focus on electrical energy consumption.

Various manufacturing parameters have been tested on three rapid prototyping systems: Thermojet (3DS), FDM 3000 (Stratasys) and EOSINT M250 Xtended (EOS). The objective is to select sets of parameters for reduction of electrical energy consumption. For this, a part is manufactured in several orientations and positions in the chamber of these RP systems. For each test, the electrical power is noted. Finally, certain rules are proposed to minimize this electrical energy consumption during a job.

It is important to minimize the manufacturing time but there is no general rule for optimization of electrical energy consumption. Each RP system must be tested with energy consumption considerations under the spotlight.

The work is only based on rapid prototyping processes. The objective is to take into consideration the complete life‐cycle of a rapid prototyped part: manufacturing of raw material as far as reprocessing of waste.

Reduction of electrical energy consumption to complete a job.

Currently, environmental aspects are not well studied in rapid prototyping.

Striving to achieve the goal of carbon neutrality before 2060 indicates that China, as the most extensive power system in the world and a country based on coal power, is imperative to improve the technical level of electric power utilization. This paper aims to explore the nonlinear evolution mechanism of power technology progress under the constraints of net-zero carbon dioxide emissions in China.

This paper, first, based on China’s provincial panel data from 2000 to 2019, uses global direction distance function to measure power technological progress. Second, the threshold regression model is used to explore the nonlinear relationship between carbon emission reduction constraints on electric power technological progress.

There is a significant inverted U-shaped relationship between China’s provincial carbon emission reduction constraints and electric power technological progress. Meanwhile, the scale of regional economic development has a significant moderating effect on the relationship between carbon emission reduction constraints and power technological progress.

This paper puts forward targeted suggestions for perfecting regional carbon emission reduction policy and improving electric power technological progress.

Based on the global directional distance function, this paper extracts power as a production factor in total factor productivity and calculates the total factor electric power technological progress. This paper objectively reveals the influence mechanism of carbon emission reduction constraints on electric power technology progress based on the threshold regression model.

The purpose of this study is to analyze the nonlinear relationship between electricity consumption (EC) and electricity transmission losses (ETL) in Ghana. Also, we examined how ETL moderate the effect of EC on economic growth in Ghana from 1980 to 2021.

We used timeseries data from 1980 to 2021 within an autoregressive distributed lag framework to analyze the links among ETL, EC and economic growth in Ghana.

Findings show the existence of an asymmetric long-run relationship between EC and ETL. Also, the negative effects of ETL on EC are bigger in the long run. In addition, ETL and EC combine to reduce economic growth, in the long run, providing evidence for the energy-led growth theory in Ghana. Population and inflation were also found to have a significant effect on economic growth in Ghana.

We examined the nonlinear nexus of EC and ETL, which extant studies have ignored in discussing the link between EC and economic growth. Again, we showed that ETL reduces EC causing a reduction in economic growth.

Thermal protection of a flange is critical for preventing tower icing and collapse of wind turbines (WTs) in extremely cold weather. This study aims to develop a novel thermal protection system for the WTs flanges using an electrical heat-tracing element.

A three-dimensional model and the Poly-Hexacore mesh structure are used, and the fluid-solid coupling method was validated and then deployed to analyze the heat transfer and convection process. Intra-volumetric heat sources are applied to represent the heat generated by the heating element, and the dynamic boundary conditions are considered. The steady temperature and temperature uniformity of the flange are the assessment criteria for the thermal protection performance of the heating element.

Enlarging the heating area and increasing the heating power improved the flange's temperature and temperature uniformity. A heating power of 4.9 kW was suitable for engineering applications with the lowest temperature nonuniformity. Compared with continuous heating, the increased temperature nonuniformity was buffered, and the electrical power consumption was reduced by half using pulse heating. Pulse heating time intervals of 1, 3 and 4 h were determined for the spring, autumn and winter, respectively.

The originality of this study is to propose a novel electrical heat-tracing thermal protection system for the WTs flanges. The effect of different arrangements, heating powers and heating strategies was studied, by which the theoretical basis is provided for a stable and long-term utilization of the WT flange.

Women in Western Europe wore empire style robes which were made with a light and thin fabric revealing their body. To stress the silhouette of their body, they applied oil to it or sprayed water on the robe so that it would cling to the body, and most women suffered from muslin disease, meaning flu and tuberculosis of the lungs in winter season. The purpose of this paper is to examine the thermal insulation of the robe with spencer jacket in dry and wet environment through thermal manikin experiments.

Three kinds of spencer jacket were made based on historical evidence and data, and experimental work for thermal insulation was conducted using a thermal manikin. The study measured the total thermal resistance of dress-jacket set: weight of the clothing before and after wetting, thermal insulation of the spencer jackets and set of clothing in dry and wet conditions, electric power consumption of the set of clothing in the wet condition and temperature inside the clothing and surface temperature of the wet set of clothing.

The thermal insulation of the robe with spencer jacket in the wet condition was in the range of 0.135-0.144 clo, which was about 80 percent lower than the range of values of 0.73-0.79 clo measured in the dry condition. This means that women felt uncomfortable in wetting condition or raining environment even when wearing the robe with a spencer jacket. Thermal insulation of clothing was dependent to the air gap under garment, clothing layers, ventilation through fabric and body part.

In this study, the thermal insulation of an empire style robe with spencer jacket in wet condition was measured using a dry thermal manikin, not with the sweating manikin. The authors measure the electric power consumption according to drying time of the clothing set at the body parts. In order to study the effect of different materials and clothing wetting, comparison experiments were conducted in dry and wet conditions using the rinse cycle of washing machine.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Natural gas (NG) consumption is increasing at an alarming rate, and more than 50 percent of this gas is used in generating electric power (EP) and desalted seawater (DW) in what is called cogeneration power desalting plants (CPDP). In this context, the purpose of this paper is to study the energy status in Qatar. More specifically, Qatar energy supply side is studied through the CPDP. In an effort to decrease the energy consumption by raising the efficiency of the CPDP (at the supply side), and conserving energy at the demand side, primarily energy footprint is performed.

The status of CPDP, in terms of the type, capacity, fuel consumption is studied, and measures that should be taken to improve their performance and reduce their consumed fuel are presented.

Study of the EP and DW sector showed that the fuel consumed in the CPDP can be reduced substantially by converting the simple gas turbine cycle to combined cycle to raise its efficiency from 30 percent to up to 50 percent. On the demand side, it was shown that air conditioning (AC) of buildings consumes about two‐third of the summer peak load; and about half of the annual EP output. So, measures to decrease the AC cooling load are suggested by implementing building code to decrease the consumed energy in buildings.

Data on the NG and oil proven reserve, production, and consumption are limited and scattered. It is necessary to have a clear picture of Qatar showing the flow of energy supply, demand, consumption, and losses in certain sectors to know where energy can be conserved; and this paper is the first trial in that direction. Although prime energy looks abundant today, it can be consumed locally within few decades if consumption is not controlled.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.