Multidimensional Perspectives for Energy Poverty Sustainable Mitigation

[...]Karpinska et al. address the subject from a regional perspective in Poland and show the spatial distribution of vulnerability to energy poverty in this territory while investigating the influence of different vulnerability factors (contribution number 2). Looking to the future, Castaño-Rosa et al. use climate change scenarios to analyze climate vulnerability in seven different national contexts, framing it in the context of future summer energy poverty (contribution number 4). A different approach is taken by Montes-Villalva et al., where a methodology is proposed to evaluate the impact of urban planning on indoor lighting demand and its relationship with energy poverty levels, along with the potential implications of policies directed toward influencing high-rise densification and re-densification in urban areas (contribution number 5).

Exploring the Relationship between Energy and Food Security in Africa with Instrumental Variables Analysis

The well-being of human populations and their sustainable development are strongly predicated on energy and food security. This is even more true of Africa due to often suboptimal food production, undernourishment, and extreme poverty. This article researches the relationship between energy and food security using Cobb–Douglas production functions based on the World Development Indicators data for 28 African countries. The methodological approach includes cross-sectional dependence and unit root tests, instrumental variables two-stage least-squares and generalized method of moments, and panel Driscoll–Kraay standard errors. Results suggest that the promotion of energy security promotes food security. This is possible because food production and distribution are energy-intensive. Therefore, energy is fundamental to achieving food security and zero hunger. The availability, affordability, accessibility, and acceptability of energy can thus help to fix the growing agricultural production shortage in Africa. An important policy focus should be on achieving energy security.

Accounting the factor of randomity of social processes in prediction of demand for electric energy

Мета. Урахування фактору випадковосл сощальних процесш при прогнозуванш попиту на електричну енерпю для зменшення похибки. Методика. Апарат математично! статистики, методш лшшного програмування, теорп нечггких множин i методiв експертного оцшювання, теорй' шкал, Байесовський п1дх1д до моделей прогнозування, комп'ютерне моделювання. Результаты. Проаналiзована динамiка споживання електрично! енергп за рiзнi перiоди часу, встановлено вплив фактору пандемп на процес формування попиту на електричну енерпю. Розроблена вербально-числова шкала для комплексного оцшювання впливу на попит на електричну енерпю такого складного сощального явища, як пандемш. Сформована модель прогнозування попиту на електричну енерпю з використанням Байесовського подходу та ощнки експерта, що дозволила використати ретроспективш данi споживання електрично! енергп та врахувати невизначенiсть соцiального фактору впливу пандемп. Наукова новизна. Набула подальшого розвитку модель прогнозування попиту на електричну енерпю, яка, на вщмшу в1д iнших, ураховуе фактор випадковостi соцiальних процеив i вербально-числову шкалу, що дозволяе зменшити похибку прогнозування споживання електрично! енергп. Практична значимтсть. Результата дослщження кориснi для пщприемств, що спецiалiзуються на генерацй', передачi й розподшу електрично! енергп споживачам. Представленi результата надають можливють зменшити похибку прогнозування попиту на електричну енерпю при врахуванш фактору випадковосл сощальних процешв.Alternate :Purpose. Taking into account the factor of randomness of social processes when forecasting the demand for electric energy to reduce the error. Methodology. Apparatus of mathematical statistics, linear programming methods, fuzzy set theory and expert assessment methods, scale theory, Bayesian approach to forecasting models, computer modeling. Findings. The dynamics of consumption of electric energy for different periods of time is analyzed, the influence of the pandemic factor on the process of formation of demand for electric energy is established. A verbal-numerical scale has been developed for a comprehensive assessment of the impact on the demand for electric energy of such a complex social phenomenon as a pandemic. A model for forecasting the demand for electrical energy was formed using the Bayesian approach and an expert's assessment, which made it possible to use retrospective data on electrical energy consumption and take into account the uncertainty of the social factor influencing the pandemic. Originality. The model for forecasting the demand for electrical energy has been further developed, which, unlike others, takes into account the factor of randomness of social processes and a verbal-numerical scale, which makes it possible to reduce the error in predicting the consumption of electrical energy. Practic l value. The research results are useful for enterprises specializing in the generation, transmission and distribution of electrical energy to consumers. The presented results make it possible to reduce the error in forecasting the demand for electric energy, taking into account the factor of randomness of social processes.

Analysis on the effect of home appliances use to electricity consumption during WFH

Various forms of quarantine and social restrictions to prevent the spread of the COVID-19 pandemic has impacted lifestyle. WFH has emerged as a growing trend in this new normal era. The implementation of WFH results in all work-related activities becoming home-centred. This study aims to analyse the pheasant and find out the effect of household appliance use on energy consumption during the implementation of WFH. The questionnaire survey was conducted in April 2020 and it collected 215 responses from residents of Indonesia, during the pandemic which was distributed online. The data were analysed using a multiple regression analysis with SPSS to determine the relationship between variables. The results showed that the length of WFH time affected an increase of energy consumption especially from the use of home computer, internet, rice cooker, AC, and water needs. In addition, the increase that happened also depends on the population living and the size of the building they live in. If this continues, it could lead to the risk of an energy crisis. Strategies related to proper distribution and energy demand are needed to build resilience and form a sustainable society.

Machine Learning and Data Mining Applications in Power Systems

The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency;dynamic optimization of grid operations;demand response;incorporation of demand-side resources and integration of energy-efficient resources;distribution automation;and integration of smart appliances and consumer devices. [...]with reference to presented papers, we have seen a broad spectrum of data-mining and modern machine-learning techniques applied to recent problems of operation of power systems. A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements.