Supplying Energy to Vulnerable Segments of the Population: Macro-Financial Risks and Public Welfare

Smirnova, Elena; Szczepańska-Woszczyna, Katarzyna; Yessetova, Saltanat; Samusenkov, Vadim; Rogulin, Rodion

doi:10.3390/en14071834

“…The study found out that there are four major factors influence the adoption of renewable energy technology in Poland. These include environmental concern, ease of use, financial incentives, and relative advantage [26,55,56].…”

Section: Conclusion Implications and Policy Recommendationsmentioning

confidence: 99%

Evaluating Consumers’ Adoption of Renewable Energy

Khalid

¹

,

Urbański

²

,

Kowalska-Sudyka

³

et al. 2021

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The purpose of this study was to evaluate the consumers’ adoption of renewable energy in Poland. The study focused on finding out the factors that influence the adoption of the technology, considering its importance in conserving the environment. The study was conducted using a quantitative method, with primary data collected from 467 households using renewable energy technology in Poland. The research adopted the TAM model. The independent variables of the study included renewable energy initial cost, environmental concern, risk and trust for renewable energy, ease of use, financial incentives, and relative advantage. The dependent variable was renewable energy adoption. Structural equation modelling (SEM) was used to analyze the study hypotheses. The research found out that environmental concerns, ease of use, financial incentives, and relative advantage have a positive and significant influence on adoption of renewable energy technology in Poland. However, renewable energy initial cost and risk, and trust for renewable energy did not significantly influence renewable energy adoption. The study recommended that the stakeholders should consider the aspects of environmental concern as a key role player in pushing for adoption of renewable energy. The government, non-governmental organizations, and concerned stakeholders should consider giving incentives towards renewable energy adoption. Creating awareness regarding the benefits and strengths of renewable energy should be prioritized to the households.

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“…Government is also supporting the implementation of renewable energy sources Ulewicz et al, 2021) and suggests to improve the usage of natural resources in an efficient manner (Haseeb et al, 2021;Deja et al, 2019). The effective implementation of renewable energy sources helps the countries considered as vulnerable segment who are living without proper energy supply (Smirnova, Szczepańska-Woszczyna, et al, 2021) as well as the country like Ukraine (Sabishchenko et al, 2020). Smart grid consists of smart meters, load control switches, distribution boards, renewable energy resources, energy storage, computers, etc.…”

Section: Introductionmentioning

confidence: 99%

Random forest based power sustainability and cost optimization in smart grid

Durairaj¹,

Wróblewski

²

,

Joseph³

et al. 2022

Production Engineering Archives

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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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“…Government is also supporting the implementation of renewable energy sources [2] and suggests to improve the usage of natural resources in an efficient manner [3]. The effective implementation of renewable energy sources helps the countries considered as vulnerable segment who are living without proper energy supply [4] as well as the country like Ukraine [5]. Smart grid consists of smart meters, load control switches, distribution boards, renewable energy resources, energy storage, computers, etc.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

Durairaj¹,

Wróblewski²,

Sheela³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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Supplying Energy to Vulnerable Segments of the Population: Macro-Financial Risks and Public Welfare

Cited by 7 publications

References 44 publications

Evaluating Consumers’ Adoption of Renewable Energy

Evaluating Consumers’ Adoption of Renewable Energy

Random forest based power sustainability and cost optimization in smart grid

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

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