Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring.
石灰石开采是印度一项重要的经济活动，占GDP的10%左右，然而，它有一定的负面环境后果。本研究的目的是利用遥感数据集、光谱指数和机器学习算法确定圈养石灰石矿山的空间分布面积，并将其面积估算与2019财政年度的工业现场调查报告进行比较。研究区石灰石资源面积2226.16 ha, 2019年挖掘面积487.10 ha。在本研究中，我们利用高分辨率Sentinel-2A卫星数据集，在QGIS 3.18软件工具中实现归一化植被指数(NDVI)、迭代自组织数据分析技术(ISODATA)、k近邻(KNN)和随机森林(RF)算法，绘制和计算了活跃矿区。该分类器估计石灰石矿区面积为379.57 ha，用户精度(UA)为97.25%，生产者精度(PA)为99.18%，kappa系数为0.957。KNN法估算矿区面积为417.47 ha, UA为98.99%，PA为99.10%，kappa值为0.947;NDVI法估算矿区面积为469.92 ha, UA为93.63%，PA为92.04%，kappa值为0.685。本研究证实，RF分类器对KNN分类器(OA为94.78%)、NDVI分类器(OA为79.84%)的分类总体准确率(OA)为95.79%，而ISODATA分类器的分类准确率较差，OA为64.16%。本研究有助于石灰石矿主和环境工程师做出环境可持续的决策、生态友好型矿山设计和监测。
{"title":"Assessment of open-pit captive limestone mining areas using sentinel-2 imagery with spectral indices and machine learning algorithms","authors":"V. C., U. G","doi":"10.3233/kes-230065","DOIUrl":"https://doi.org/10.3233/kes-230065","url":null,"abstract":"Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77799449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multicollinearity occurs when there comes a high level of correlation between the independent variables. This correlation creates the problem because the independent variables should be independent. Higher the degree of correlation means more complex problems you will face while fitting the model and interpreting the results. In this paper, we have eliminated the problem of multicollinearity on the basis of Hatvalues. The variables with higher Hatvalues will be removed from the data before fitting the model. This paper presents the comparison of results achieved by the proposed technique and state of the art methods.
{"title":"Detection and elimination of multicollinearity in regression analysis","authors":"Preeti Singh, Sarvpal H. Singh, M. Paprzycki","doi":"10.3233/kes-221622","DOIUrl":"https://doi.org/10.3233/kes-221622","url":null,"abstract":"Multicollinearity occurs when there comes a high level of correlation between the independent variables. This correlation creates the problem because the independent variables should be independent. Higher the degree of correlation means more complex problems you will face while fitting the model and interpreting the results. In this paper, we have eliminated the problem of multicollinearity on the basis of Hatvalues. The variables with higher Hatvalues will be removed from the data before fitting the model. This paper presents the comparison of results achieved by the proposed technique and state of the art methods.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80941187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melike Toslak, A. Ulutaş, Salim Ürea, Željko Stević
Production enterprises are enterprises that produce goods or services that aim to meet human needs such as machinery-equipment materials and labour. In order for a manufacturing enterprise to carry out its activities successfully, it must make the right choice when choosing its inputs. The correct execution of production activities and the selection of machinery, which requires high capital investments, also affect the efficiency of the enterprises, the correct use of materials and their costs. Therefore, it is an important decision for business managers to choose the right machine. At this stage, multi-criteria decision making (MCDM) methods are used for choosing the right machine. MCDM methods are methods used in the evaluation of alternatives using more than one criterion. In addition, the MCDM method is used in machine selection as well as in many areas. In this study, PSI, SV and MARCOS methods, which are among the MCDM methods, were used for peanut butter machine selection. First, the criteria and alternatives to be used for the peanut butter machine selection were determined by interviewing a peanut butter factory manager. In the study, while the criteria weights were determined, PSI and SV methods were used, while the machines were ranked with the MARCOS method. In addition, the MARCOS method was compared with other MCDM methods such as PIV, CODAS and WEDBA methods. After the rankings were found according to the methods, the relations between the rankings were examined using the Spearman Correlation method. The main purpose of the study is to determine the suitable butter machine for a peanut paste production factory. Contribution of this study to the literature PSI, SV and MARCOS methods were used together for the first time. In addition, no study has been found in the literature related to peanut butter machine. Therefore, this study is original and contributes to the literature.
{"title":"Selection of peanut butter machine by the integrated PSI-SV-MARCOS method","authors":"Melike Toslak, A. Ulutaş, Salim Ürea, Željko Stević","doi":"10.3233/kes-230044","DOIUrl":"https://doi.org/10.3233/kes-230044","url":null,"abstract":"Production enterprises are enterprises that produce goods or services that aim to meet human needs such as machinery-equipment materials and labour. In order for a manufacturing enterprise to carry out its activities successfully, it must make the right choice when choosing its inputs. The correct execution of production activities and the selection of machinery, which requires high capital investments, also affect the efficiency of the enterprises, the correct use of materials and their costs. Therefore, it is an important decision for business managers to choose the right machine. At this stage, multi-criteria decision making (MCDM) methods are used for choosing the right machine. MCDM methods are methods used in the evaluation of alternatives using more than one criterion. In addition, the MCDM method is used in machine selection as well as in many areas. In this study, PSI, SV and MARCOS methods, which are among the MCDM methods, were used for peanut butter machine selection. First, the criteria and alternatives to be used for the peanut butter machine selection were determined by interviewing a peanut butter factory manager. In the study, while the criteria weights were determined, PSI and SV methods were used, while the machines were ranked with the MARCOS method. In addition, the MARCOS method was compared with other MCDM methods such as PIV, CODAS and WEDBA methods. After the rankings were found according to the methods, the relations between the rankings were examined using the Spearman Correlation method. The main purpose of the study is to determine the suitable butter machine for a peanut paste production factory. Contribution of this study to the literature PSI, SV and MARCOS methods were used together for the first time. In addition, no study has been found in the literature related to peanut butter machine. Therefore, this study is original and contributes to the literature.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89648699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With a growing focus from the United Nations to eradicate the ill effects of climate change, countries around the world are transforming to green and sustainable habits/practices. Adoption of clean energy for demand satisfaction is a prime focus of many countries as it reduces carbon trace and promotes global development. In developing countries like India, there is an urge for sustainable global development. Literature shows that direct and complete adoption of clean energy incurs some barriers, which impede the sustainable development of the nation. Grading such barriers supports policymakers to effectively plan strategies, which motivates authors to put forward a novel decision model with integrated approaches. First, qualitative rating data on barriers and circular economy (CE) factors are collected from experts via questionnaires, which are transformed into q-rung orthopair fuzzy information (qRFI). Second, the weights of experts and CE factors are determined by the proposed variance measure and CRITIC. Third, barriers are graded by the proposed ranking algorithm that considers modified WAPAS formulation. Finally, these approaches are integrated into a model that is testified for practicality by using a case example from India. Sensitivity and comparative analyses are performed to realize the merits and limitations of the model for extant works.
随着联合国越来越重视消除气候变化的不良影响，世界各国正在向绿色和可持续的习惯/做法转变。采用清洁能源满足需求是许多国家的主要关注点，因为它减少了碳足迹，促进了全球发展。在印度这样的发展中国家，人们迫切需要全球可持续发展。文献表明，直接和完全采用清洁能源会产生一些障碍，阻碍国家的可持续发展。对这些障碍进行分级有助于决策者有效地规划策略，这促使作者提出了一种综合方法的新决策模型。首先，通过问卷调查的方式收集专家对壁垒和循环经济因素的定性评价数据，并将其转化为q-rung orthopair fuzzy information (qRFI)。其次，通过提出的方差度量和CRITIC来确定专家和CE因素的权重。第三，通过考虑改进的WAPAS公式提出的排序算法对障碍进行分级。最后，将这些方法整合到一个模型中，并通过印度的一个案例验证了该模型的实用性。通过敏感性分析和对比分析，认识到该模型在现有文献中的优点和局限性。
{"title":"Solving barrier ranking in clean energy adoption: An MCDM approach with q-rung orthopair fuzzy preferences","authors":"R. Krishankumar, D. Pamučar","doi":"10.3233/kes-230048","DOIUrl":"https://doi.org/10.3233/kes-230048","url":null,"abstract":"With a growing focus from the United Nations to eradicate the ill effects of climate change, countries around the world are transforming to green and sustainable habits/practices. Adoption of clean energy for demand satisfaction is a prime focus of many countries as it reduces carbon trace and promotes global development. In developing countries like India, there is an urge for sustainable global development. Literature shows that direct and complete adoption of clean energy incurs some barriers, which impede the sustainable development of the nation. Grading such barriers supports policymakers to effectively plan strategies, which motivates authors to put forward a novel decision model with integrated approaches. First, qualitative rating data on barriers and circular economy (CE) factors are collected from experts via questionnaires, which are transformed into q-rung orthopair fuzzy information (qRFI). Second, the weights of experts and CE factors are determined by the proposed variance measure and CRITIC. Third, barriers are graded by the proposed ranking algorithm that considers modified WAPAS formulation. Finally, these approaches are integrated into a model that is testified for practicality by using a case example from India. Sensitivity and comparative analyses are performed to realize the merits and limitations of the model for extant works.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80707866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Preeti Devi, Bartłomiej Kizielewicz, A. Guleria, A. Shekhovtsov, N. Gandotra, Namita Saini, W. Sałabun
The notion of soft matrix plays a vital role in many engineering applications and socio-economic and financial problems. A picture fuzzy set has been used to handle uncertainty data in modeling human opinion. In this work, we recall the picture fuzzy soft matrix concept and its different subsequent classes. Also, different kinds of binary operations over the proposed matrices have been provided. The main contribution of this paper is that using the concept of choice matrix and its weighted form and the score matrix, a new algorithm for decision-making has been outlined by considering the picture of fuzzy soft matrices. The current challenge In the decision-making problems is that many qualitative and quantitative criteria are involved. Hence, the dimensionality reduction technique plays an essential role in simplicity and broader applicability in the decision-making processes. We present an algorithm for the reduction process using the proposed definitions of the object and parameter-oriented picture fuzzy soft matrix and the technique to find the threshold value for the provided information. Then, illustrative numerical examples have also been provided for each proposed algorithm. A detailed comparative study of the proposed techniques has also been carried out in contrast with other existing techniques.
{"title":"Dimensionality reduction technique under picture fuzzy environment and its application in decision making","authors":"Preeti Devi, Bartłomiej Kizielewicz, A. Guleria, A. Shekhovtsov, N. Gandotra, Namita Saini, W. Sałabun","doi":"10.3233/kes-230031","DOIUrl":"https://doi.org/10.3233/kes-230031","url":null,"abstract":"The notion of soft matrix plays a vital role in many engineering applications and socio-economic and financial problems. A picture fuzzy set has been used to handle uncertainty data in modeling human opinion. In this work, we recall the picture fuzzy soft matrix concept and its different subsequent classes. Also, different kinds of binary operations over the proposed matrices have been provided. The main contribution of this paper is that using the concept of choice matrix and its weighted form and the score matrix, a new algorithm for decision-making has been outlined by considering the picture of fuzzy soft matrices. The current challenge In the decision-making problems is that many qualitative and quantitative criteria are involved. Hence, the dimensionality reduction technique plays an essential role in simplicity and broader applicability in the decision-making processes. We present an algorithm for the reduction process using the proposed definitions of the object and parameter-oriented picture fuzzy soft matrix and the technique to find the threshold value for the provided information. Then, illustrative numerical examples have also been provided for each proposed algorithm. A detailed comparative study of the proposed techniques has also been carried out in contrast with other existing techniques.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90170997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing need of utilizing unstructured knowledge embedded in open-domain natural language text into machine-processable forms requires the induction of hardly extracted structured knowledge into knowledge bases which makes the Semantic Web vision a reality. In this context, ontologies, and ontological knowledge (triples) plays a vital role. This research introduces two novel concepts named Directed Collocation (DC) and Joined Directed Collocation (JDC) along with a methodical application of them to infer new ontological knowledge. Introduced Quality-Threshold-Value (QTV) parameter improves the quality of the inferred ontological knowledge. Having set a moderate value (3) for QTV, this approach inferred 95,491 new ontological knowledge from 43,100 triples of open domain Sri Lankan English news corpus. Indeed, the outcome was approximately doubled in size as the source corpus. Some inferred ontological knowledge was identical with the original corpus content, which evidences the accuracy of this approach. The remaining were validated using inter-rater agreement method (high reliability) and out of which around 56% were estimated as effective. The inferred outcome which is in the triple format may use in any knowledge base. The proposed approach is domain independent. Thus, helps to construct/extend ontologies for any domain with the help of less or no human specialists.
{"title":"Ontological knowledge inferring approach: Introducing Directed Collocations (DC) and Joined Directed Collocations (JDC)","authors":"Muditha Tissera, R. Weerasinghe","doi":"10.3233/kes-221516","DOIUrl":"https://doi.org/10.3233/kes-221516","url":null,"abstract":"The growing need of utilizing unstructured knowledge embedded in open-domain natural language text into machine-processable forms requires the induction of hardly extracted structured knowledge into knowledge bases which makes the Semantic Web vision a reality. In this context, ontologies, and ontological knowledge (triples) plays a vital role. This research introduces two novel concepts named Directed Collocation (DC) and Joined Directed Collocation (JDC) along with a methodical application of them to infer new ontological knowledge. Introduced Quality-Threshold-Value (QTV) parameter improves the quality of the inferred ontological knowledge. Having set a moderate value (3) for QTV, this approach inferred 95,491 new ontological knowledge from 43,100 triples of open domain Sri Lankan English news corpus. Indeed, the outcome was approximately doubled in size as the source corpus. Some inferred ontological knowledge was identical with the original corpus content, which evidences the accuracy of this approach. The remaining were validated using inter-rater agreement method (high reliability) and out of which around 56% were estimated as effective. The inferred outcome which is in the triple format may use in any knowledge base. The proposed approach is domain independent. Thus, helps to construct/extend ontologies for any domain with the help of less or no human specialists.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86937291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid growth of social media and specialized websites that provide critical product reviews has resulted in a massive collection of information for customers worldwide. These data could contain a wealth of information, such as product reviews, market forecasting, and the polarity of sentiments. In these challenges, machine learning and deep learning algorithms give the necessary capabilities for sentiment analysis. In today’s competitive markets, it’s critical to grasp reviewer opinions and sentiments by extracting and analyzing their characteristics. The research aims to develop an optimised model for evaluating sentiments and categorising them into proper categories. This research proposes a unique, novel hybridised model that integrates the advantages of deep learning methods Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with word embedding technique. The performance of different word embedding techniques is compared to select the best embedding for the implementation in the proposed model. Furthermore, a multi-convolution approach with attention-oriented BiLSTM is applied. To test the validity of the performance of the proposed model, standard metrics were applied. The outcome indicates that the suggested model achieves a significantly improved accuracy of 96.56%, superior to other models.
{"title":"CoBiCo: A model using multi-stage ConvNet with attention-based Bi-LSTM for efficient sentiment classification","authors":"R. Ranjan, A. Daniel","doi":"10.3233/kes-230901","DOIUrl":"https://doi.org/10.3233/kes-230901","url":null,"abstract":"The rapid growth of social media and specialized websites that provide critical product reviews has resulted in a massive collection of information for customers worldwide. These data could contain a wealth of information, such as product reviews, market forecasting, and the polarity of sentiments. In these challenges, machine learning and deep learning algorithms give the necessary capabilities for sentiment analysis. In today’s competitive markets, it’s critical to grasp reviewer opinions and sentiments by extracting and analyzing their characteristics. The research aims to develop an optimised model for evaluating sentiments and categorising them into proper categories. This research proposes a unique, novel hybridised model that integrates the advantages of deep learning methods Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with word embedding technique. The performance of different word embedding techniques is compared to select the best embedding for the implementation in the proposed model. Furthermore, a multi-convolution approach with attention-oriented BiLSTM is applied. To test the validity of the performance of the proposed model, standard metrics were applied. The outcome indicates that the suggested model achieves a significantly improved accuracy of 96.56%, superior to other models.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85500028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we discuss the role of microgrids as a “prosumer”. Microgrids are used to provide locally generated power (energy), and this concept is becoming increasingly prominent with time. Microgrids have added economic value when assuming the role of “prosumer” or “group of prosumers”. A new outlook in managing prosumers connected to the energy sharing network has led to the creation of prosumer coalition groups, which can subsequently manage numerous goals in microgrid energy systems. For achieving prosumer energy goals, Goal-Oriented Requirements Engineering (GORE) is deployed in this work. Hence, the purpose of this research is to develop prosumer coalition-GORE artefacts, strategising GORE players, modelling non-functional requirements and ensuring sustainable requirements engineering management in the microgrid energy system. In this research, an i* goal model has been used to design a payoff function based on the game theory concept. The key to the pricing function is its fair distribution of payoffs depending on their surplus energy generation, thus providing optimum satisfaction to the buyer. With the objective of maximising the profits earned by prosumers through intra-microgrid energy trading, this paper also designs multi-objective functions to provide optimal value by using the i* goal model. By integrating Java with the IBM CPLEX optimisation tool, a simulation model based on the proposed method was developed and analysed. The results show that the proposed approach yields better outcomes when meeting the requirements of fairness and efficiency, reducing the intermittency effect of generation through renewable resources.
{"title":"Multi-agent based coalition formation of prosumers in microgrids using the i* goal modelling","authors":"Sreenithya Sumesh, A. Krishna","doi":"10.3233/kes-230902","DOIUrl":"https://doi.org/10.3233/kes-230902","url":null,"abstract":"In this paper, we discuss the role of microgrids as a “prosumer”. Microgrids are used to provide locally generated power (energy), and this concept is becoming increasingly prominent with time. Microgrids have added economic value when assuming the role of “prosumer” or “group of prosumers”. A new outlook in managing prosumers connected to the energy sharing network has led to the creation of prosumer coalition groups, which can subsequently manage numerous goals in microgrid energy systems. For achieving prosumer energy goals, Goal-Oriented Requirements Engineering (GORE) is deployed in this work. Hence, the purpose of this research is to develop prosumer coalition-GORE artefacts, strategising GORE players, modelling non-functional requirements and ensuring sustainable requirements engineering management in the microgrid energy system. In this research, an i* goal model has been used to design a payoff function based on the game theory concept. The key to the pricing function is its fair distribution of payoffs depending on their surplus energy generation, thus providing optimum satisfaction to the buyer. With the objective of maximising the profits earned by prosumers through intra-microgrid energy trading, this paper also designs multi-objective functions to provide optimal value by using the i* goal model. By integrating Java with the IBM CPLEX optimisation tool, a simulation model based on the proposed method was developed and analysed. The results show that the proposed approach yields better outcomes when meeting the requirements of fairness and efficiency, reducing the intermittency effect of generation through renewable resources.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88277969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Models for multiple attribute decision making with some interval-valued 2-tuple linguistic Pythagorean fuzzy Bonferroni mean operators","authors":"Jie Wang, Mao Lu, G. Wei","doi":"10.3233/kes-190417","DOIUrl":"https://doi.org/10.3233/kes-190417","url":null,"abstract":"","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89569822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"CCEE: Clustering with chicken swarm based energy efficient algorithm with APUGR protocol for mobility awareness and energy saving in adhoc network","authors":"T. Santhi Sri, J. Rajendra Prasad, R. Kiran Kumar","doi":"10.3233/kes-180380","DOIUrl":"https://doi.org/10.3233/kes-180380","url":null,"abstract":"","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78218212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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