Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.
Purpose – The purpose of this paper is to develop traits and model of entrepreneurship development from Islamic point of view. Design/methodology/approach – The study is descriptive, in so far as its goal is to describe a method, and the nature of the paper is conceptual. The study draws on secondary materials through library research. Findings – In this paper both the traits as well as model of entrepreneurship are developed from Islamic perspective. The salient traits are knowledge, initiative, risk taking, customer orientation, employee involvement, strategic thinking, fear of Allah, hard working, innovativeness, excellence, honesty and truthfulness, morality, vision, optimism, patience, social welfare, Halal earnings and economical. The model would be used for developing entrepreneurship from Islamic perspective by performing various types of activities relating to the phases of preparatory and awareness development, support and motivation and feedback. Research limitations/implications – The paper has implications for government, potential entrepreneurs and existing entrepreneurs of Muslim countries representing one fourth of the world population. It is also expected that the study will help and encourage Islamic scholars to think over the matter and make them more aware in developing entrepreneurship based on Quran and Sunna in the modern business world. Practical implications – The findings of this research can be used as a guide to develop entrepreneurship in Muslim countries from Islamic point of view. The study could have practical implications falling within the purview of social sciences such as economics, business studies, public administration, political science, development studies, sociology, law, Quranic science, industrial management, education and human resource management. Originality/value – While many studies, partially, have focussed on traditional entrepreneurship sparing the demand of Muslim world, in this paper, the authors open a new avenue contributing to the literature on entrepreneurship development from Islamic perspective. The proposed model will be of genuine interest and benefits to government as prime policymaker, existing entrepreneurs, potential entrepreneurs, Islamic scholars and academicians.
Green banking or sustainable banking is one of the issues of the concern of all stakeholders of the world. Following this concern, this study has investigated the status of green banking practices of the non-bank financial institutions (NBFIs) and commercial banks of Bangladesh. Analyzing the contents of annual reports as well as websites of banks and NBFIs, the study finds that 44 out of 57 banks and 13 out of 33 NBFIs, to a varying degree, have exposures in direct or indirect green financing. But only 45 banks and 25 NBFIs conducted environmental risk rating. Most of the banks and NBFIs practice green banking only in a limited scale and volume and disclose green banking information in a semi structured manner in both the annual reports and corporate websites. However, except one, all the 56 scheduled banks and all the 33 non-bank financial institutions (NBFIs) have their own green banking policy guidelines. They also have green office guide for conducting in-house green activities. The study finds that green banking disclosures in their annual reports exceed that in their websites. It is also found that both private commercial banks (PCBs), and foreign commercial banks (FCBs) have surpassed state-owned commercial banks (SCBs) and state-owned specialized development banks (SDBs) in terms of the green financing.
The present study is intended to develop an intelligent model for the prediction of color strength of cotton knitted fabrics using fuzzy knowledge based expert system (FKBES). The factors chosen for developing the prediction model are dye concentration, dyeing time and process temperature. Besides, such factors are nonlinear and have mutual interactions among them; so it is not easy to create an exact correlation between the inputs variables and color strength using mathematical or statistical methods. In contrast, artificial neural network and neural-fuzzy models require massive amounts of experimental data for model parameters optimization which are challenging to collect from the dyeing industries. In this context, fuzzy knowledge based expert system is the most efficient modeling tool which performs exceptionally well in a non-linear complex domain with lowest amount of trial data like human experts. In this study, laboratory scale experiments were conducted for three types of cotton knitted fabrics to verify the developed fuzzy model. It was found that actual and predicted values of color strength of the knitted fabrics were in good agreement with each other with less than 5% absolute error.
Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs.
Globally, the research on electric vehicles (EVs) has become increasingly popular due to their capacity to reduce carbon emissions and global warming impacts. The effectiveness of EVs depends on appropriate functionality and management of battery energy storage. Nevertheless, the battery energy storage in EVs provides an unregulated, unstable power supply and has significant voltage drops. To address these concerns, power electronics converter technology in EVs is necessary to achieve a stable and reliable power transmission. Although various EV converters provide significant contributions, they have limitations with regard to high components, high switching loss, high current stress, computational complexity, and slow dynamic response. Thus, this paper presents the emerging trends in analytical assessment of power electronics converter technology incorporated energy storage management in EVs. Hundreds (100) of the most significant and highly prominent articles on power converters for EVs are studied and investigated, employing the Scopus database under predetermined factors to explore the emerging trends. The results reveal that 57% of articles emphasize modeling, experimental work, and performance evaluation. In comparison, 13% of papers are based on problem formulation and simulation analysis, and 8% of articles are survey, case studies, and review-based. Besides, four countries, including China, India, the United States, and Canada, are dominant to publish the maximum articles, indicating 33, 17, 14, and 13, respectively. This review adopts the analytical assessment that outlines various power converters, energy storage, controller, optimization, energy efficiency, energy management, and energy transfer, emphasizing various schemes, key contributions, and research gaps. Besides, this paper discusses the drawbacks and issues of the various power converters and highlights future research opportunities to address the existing limitations. This analytical assessment could be useful to EV engineers and automobile companies towards the development of advanced energy storage management interfacing power electronics for sustainable EV applications.
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