Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10214992
J. Jayakody, W. Wijayanayake
DevOps is welcomed by software development companies in recent years as a novel approach attached to the Agile software development methodology. Yet, they are in trouble with implementing DevOps because it doesn’t just concentrate on technological changes. It alters the software development process more broadly. To assist this challenging process, DevOps maturity models have been established by a few scholars in recent years. Nevertheless, those models consist variety of drawbacks as; the majority of them have not been properly evaluated and published. This research aimed to provide a critical evaluation of the data available in existing studies on the DevOps maturity models and to propose a DevOps adoption process improvement framework that is validated by industry practitioners. To accomplish this target, a systematic literature review was applied and studied the available DevOps maturity models, weaknesses, and strengths of those models. A new framework for DevOps process improvement is developed by monitoring and contrasting the available data. Furthermore, it was assessed by an interview survey to strengthen the research’s overall goal. The study presents a verified DevOps process improvement model which consists of four main DevOps success areas; DevOps practices, DevOps team, DevOps culture, and DevOps measurement. Each area follows five maturity levels starting with beginning to expert. This framework assists software development companies in obtaining benefits while reducing the difficulties associated with DevOps adoption.
{"title":"Process Improvement Framework for DevOps Adoption in Software Development","authors":"J. Jayakody, W. Wijayanayake","doi":"10.1109/SCSE59836.2023.10214992","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10214992","url":null,"abstract":"DevOps is welcomed by software development companies in recent years as a novel approach attached to the Agile software development methodology. Yet, they are in trouble with implementing DevOps because it doesn’t just concentrate on technological changes. It alters the software development process more broadly. To assist this challenging process, DevOps maturity models have been established by a few scholars in recent years. Nevertheless, those models consist variety of drawbacks as; the majority of them have not been properly evaluated and published. This research aimed to provide a critical evaluation of the data available in existing studies on the DevOps maturity models and to propose a DevOps adoption process improvement framework that is validated by industry practitioners. To accomplish this target, a systematic literature review was applied and studied the available DevOps maturity models, weaknesses, and strengths of those models. A new framework for DevOps process improvement is developed by monitoring and contrasting the available data. Furthermore, it was assessed by an interview survey to strengthen the research’s overall goal. The study presents a verified DevOps process improvement model which consists of four main DevOps success areas; DevOps practices, DevOps team, DevOps culture, and DevOps measurement. Each area follows five maturity levels starting with beginning to expert. This framework assists software development companies in obtaining benefits while reducing the difficulties associated with DevOps adoption.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955871","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215003
H. Kumari, U.M.M.P.K. Nawarathne
Raisin performs a decisive role in the commodity economy. Recently, low-quality raisin products have been introduced to agricultural markets worldwide. Therefore, it is crucial to identify a suitable classification method to distinguish between varieties of raisins. Previous research has employed various traditional machine learning methods to classify commodities. However, it is challenging to quantify uncertainties through traditional machine learning models. Therefore, this study employed a Bayesian Logistic Regression (BLR) model using seven morphological features of two varieties of raisins grown in Turkey. Initially, different machine learning techniques were employed on data. After that, four priors, such as Jefferys, Laplace, Cauchy, and Gaussian, were considered, and hyperparameters were tuned using the empirical Bayes method. Marginal posterior distributions of the model parameters were estimated, and the convergence of the models was checked. Then, evaluation metrics of the BLR model with different priors were compared to those of machine learning models. According to the results, the BLR model with Gaussian prior produced the highest accuracy of 93%. Finally, it can be concluded that the BLR model with Gaussian prior provides substantially better results when classifying raisin data.
{"title":"A Bayesian Approach for Raisin Data Classification","authors":"H. Kumari, U.M.M.P.K. Nawarathne","doi":"10.1109/SCSE59836.2023.10215003","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215003","url":null,"abstract":"Raisin performs a decisive role in the commodity economy. Recently, low-quality raisin products have been introduced to agricultural markets worldwide. Therefore, it is crucial to identify a suitable classification method to distinguish between varieties of raisins. Previous research has employed various traditional machine learning methods to classify commodities. However, it is challenging to quantify uncertainties through traditional machine learning models. Therefore, this study employed a Bayesian Logistic Regression (BLR) model using seven morphological features of two varieties of raisins grown in Turkey. Initially, different machine learning techniques were employed on data. After that, four priors, such as Jefferys, Laplace, Cauchy, and Gaussian, were considered, and hyperparameters were tuned using the empirical Bayes method. Marginal posterior distributions of the model parameters were estimated, and the convergence of the models was checked. Then, evaluation metrics of the BLR model with different priors were compared to those of machine learning models. According to the results, the BLR model with Gaussian prior produced the highest accuracy of 93%. Finally, it can be concluded that the BLR model with Gaussian prior provides substantially better results when classifying raisin data.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133605759","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215037
Dinusha Chamanthi, Chamishka Thalpawila, Rashmi Lakshani, Gowthaman Uththaman, N. Nagendrakumar, N. Karunarathna
In the modern world, the concept of the green supply chain is applied to introduce sustainable development and integrate it into production and operational management. Green standards and principles have sparked the interest of managers and professionals in selecting innovative practices for suppliers and organizations. Accordingly, this study aims to evaluate the impact of green supply chain management (GSCM) practices (reverse logistics, eco-design, green purchasing, internal environmental management, investment recovery, cooperation with customers, and green manufacturing) on the organizational performance of the Sri Lankan hotel industry. The empirical evidence verifies that adopting GSCM practices has a substantial positive impact on overall organizational performance. The study provided valuable insights into the types of GSCM practices that firms should adopt to enhance organizational performance. Moreover, this current study contributed to advancing the comprehension of the impact of GSCM practices on organizational performance. This review has the potential limitation of focusing only on the hotel industry within the service sector.
{"title":"Impact of Green Supply Chain Practices on Organizational Performance of the Hotel Industry in Sri Lanka: A Systematic Literature Review","authors":"Dinusha Chamanthi, Chamishka Thalpawila, Rashmi Lakshani, Gowthaman Uththaman, N. Nagendrakumar, N. Karunarathna","doi":"10.1109/SCSE59836.2023.10215037","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215037","url":null,"abstract":"In the modern world, the concept of the green supply chain is applied to introduce sustainable development and integrate it into production and operational management. Green standards and principles have sparked the interest of managers and professionals in selecting innovative practices for suppliers and organizations. Accordingly, this study aims to evaluate the impact of green supply chain management (GSCM) practices (reverse logistics, eco-design, green purchasing, internal environmental management, investment recovery, cooperation with customers, and green manufacturing) on the organizational performance of the Sri Lankan hotel industry. The empirical evidence verifies that adopting GSCM practices has a substantial positive impact on overall organizational performance. The study provided valuable insights into the types of GSCM practices that firms should adopt to enhance organizational performance. Moreover, this current study contributed to advancing the comprehension of the impact of GSCM practices on organizational performance. This review has the potential limitation of focusing only on the hotel industry within the service sector.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"51 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115553197","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10214995
Sachini Ginige, C. Rajapakse, Dinesh Asanka, Thilini Mahanama
In the modern connected era, the telecommunications sector plays a critical role in enabling efficient business operations across all industries. However, defaulting customers who fail to pay their dues after consuming services remain a significant challenge in the industry. Defaulters pose a risk to service providers, calling for measures to lessen both the probability of occurrence as well as its impact. Early identification of defaulters through prediction is a possible solution that enables proactive measures to mitigate the risk. However, the nature of the fixed-line product segment poses additional constraints in identifying defaulters, highlighting an existing knowledge gap. The research aims to evaluate the effectiveness of machine learning as a technique for the prediction of defaulters in the fixed-line telecommunication sector, and to develop an effective predictive model for the purpose. The success of machine learning techniques in analysis and prediction over traditional methods prompted its use in this study. The study followed the design science research methodology. An analysis was conducted based on past transaction data. Special consideration was given to the scenario of customers with little to no transaction history. Based on the analysis, a feature list for identifying defaulters was compiled, and multiple predictive models were developed and evaluated in comparison. The resulting predictive model, which uses the Random Forest technique, shows high performance in all considered aspects. The findings of the study demonstrate that machine learning techniques can effectively predict defaulters in the fixed-line telecommunication sector, with significant implications for mitigating the risk associated.
{"title":"Defaulter Prediction in the Fixed-line Telecommunication Sector using Machine Learning","authors":"Sachini Ginige, C. Rajapakse, Dinesh Asanka, Thilini Mahanama","doi":"10.1109/SCSE59836.2023.10214995","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10214995","url":null,"abstract":"In the modern connected era, the telecommunications sector plays a critical role in enabling efficient business operations across all industries. However, defaulting customers who fail to pay their dues after consuming services remain a significant challenge in the industry. Defaulters pose a risk to service providers, calling for measures to lessen both the probability of occurrence as well as its impact. Early identification of defaulters through prediction is a possible solution that enables proactive measures to mitigate the risk. However, the nature of the fixed-line product segment poses additional constraints in identifying defaulters, highlighting an existing knowledge gap. The research aims to evaluate the effectiveness of machine learning as a technique for the prediction of defaulters in the fixed-line telecommunication sector, and to develop an effective predictive model for the purpose. The success of machine learning techniques in analysis and prediction over traditional methods prompted its use in this study. The study followed the design science research methodology. An analysis was conducted based on past transaction data. Special consideration was given to the scenario of customers with little to no transaction history. Based on the analysis, a feature list for identifying defaulters was compiled, and multiple predictive models were developed and evaluated in comparison. The resulting predictive model, which uses the Random Forest technique, shows high performance in all considered aspects. The findings of the study demonstrate that machine learning techniques can effectively predict defaulters in the fixed-line telecommunication sector, with significant implications for mitigating the risk associated.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129819285","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215050
Supipi P. B. Kodithuwakku, Dinusha S. Weerasekera
In the recent decade there has been a significant increase in e-commerce platforms within the Sri-Lankan context and with the outbreak of COVID-19 the e-commerce businesses truly started to flourish and expand. E-businesses mainly use courier/parcel providers to engage in the last-mile delivery of the goods to the end customers, hence the courier services in a way act as an extension of the online brands. This study aims to identify which courier/parcel delivery service quality factors have a relationship between online shopping customer satisfaction in Colombo District with reference to the SERVQUAL model. With the reference of SERVQUAL model, the service quality factors that were relevant to the scope of the study were determined. Based on the review of the literature in this regard and with the use of convenience sampling technique, an online self-administered questionnaire was distributed among a sample of 250 within the Colombo District. The dimension empathy out of the four dimensions studied, appeared to have the highest correlation and regression, hence it is recommended that the courier/parcel delivery service providers prioritize it as a key factor when providing the courier services to the end customer. Further research is needed to identify the other service quality factors within the courier industry that could further strengthen the relationship with online shopping customer satisfaction by referring to more current literature.
{"title":"Impact of service quality factors of courier/parcel delivery industry on online shopping customer satisfaction with reference to SERVQUAL model","authors":"Supipi P. B. Kodithuwakku, Dinusha S. Weerasekera","doi":"10.1109/SCSE59836.2023.10215050","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215050","url":null,"abstract":"In the recent decade there has been a significant increase in e-commerce platforms within the Sri-Lankan context and with the outbreak of COVID-19 the e-commerce businesses truly started to flourish and expand. E-businesses mainly use courier/parcel providers to engage in the last-mile delivery of the goods to the end customers, hence the courier services in a way act as an extension of the online brands. This study aims to identify which courier/parcel delivery service quality factors have a relationship between online shopping customer satisfaction in Colombo District with reference to the SERVQUAL model. With the reference of SERVQUAL model, the service quality factors that were relevant to the scope of the study were determined. Based on the review of the literature in this regard and with the use of convenience sampling technique, an online self-administered questionnaire was distributed among a sample of 250 within the Colombo District. The dimension empathy out of the four dimensions studied, appeared to have the highest correlation and regression, hence it is recommended that the courier/parcel delivery service providers prioritize it as a key factor when providing the courier services to the end customer. Further research is needed to identify the other service quality factors within the courier industry that could further strengthen the relationship with online shopping customer satisfaction by referring to more current literature.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128188940","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}
Pub Date : 2023-06-29DOI: 10.1109/scse59836.2023.10215048
{"title":"Keynote 2: Emerging Role of AI in Sustainable Energy: Forecasting the Output of Rooftop Solar Panels","authors":"","doi":"10.1109/scse59836.2023.10215048","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215048","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127509768","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215045
K. Perera, C. Kavirathna, A. Withanaarachchi
The transport sector is currently facing significant disruptions due to the economic crisis in Sri Lanka. As a result, there is a pressing need for alternative measures within the sector. One such solution is the electrification of transportation modes, which has gained global recognition and is being considered in this study within the Sri Lankan context. The research focuses on studying various factors and their impact on the adaptability of electricity-driven solutions within the Sri Lankan transport sector, considering the current economic crisis. To identify relevant factors from previous scholarly research in this study, a systematic literature review was utilized. Subsequently, a conceptual framework was constructed to evaluate the variables. An online questionnaire survey was conducted to gather data to confirm the validity of the model using partial least square structural equation modeling. The results indicate that social and technological factors have a positive impact on the adaptability of electricity-driven solutions in the transport sector. The study also provides recommendations for fostering a better electrified transportation system.
{"title":"Factors Affecting the Electrification of Transportation Modes Amidst the Sri Lankan Economic Crisis","authors":"K. Perera, C. Kavirathna, A. Withanaarachchi","doi":"10.1109/SCSE59836.2023.10215045","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215045","url":null,"abstract":"The transport sector is currently facing significant disruptions due to the economic crisis in Sri Lanka. As a result, there is a pressing need for alternative measures within the sector. One such solution is the electrification of transportation modes, which has gained global recognition and is being considered in this study within the Sri Lankan context. The research focuses on studying various factors and their impact on the adaptability of electricity-driven solutions within the Sri Lankan transport sector, considering the current economic crisis. To identify relevant factors from previous scholarly research in this study, a systematic literature review was utilized. Subsequently, a conceptual framework was constructed to evaluate the variables. An online questionnaire survey was conducted to gather data to confirm the validity of the model using partial least square structural equation modeling. The results indicate that social and technological factors have a positive impact on the adaptability of electricity-driven solutions in the transport sector. The study also provides recommendations for fostering a better electrified transportation system.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130155151","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10214984
E.S.K. Chandrasekara, S. Vidanagamachchi
Malaria has become a widespread disease and one of the leading causes of many deaths worldwide. Malaria is a blood disease brought on by Plasmodium parasites, which are transmitted by the bite of a female Anopheles mosquito. To diagnose the condition, medical experts analyse thick and thin blood smears. However, their precision is dependent on the quality of the smear and experience in categorising and counting parasitized and uninfected cells. Such an investigation could be complicated and time-consuming for large-scale diagnosis, resulting in poor quality as well. Deep learning (DL) approaches such as Convolutional Neural Networks (CNN) offer highly scalable and improved performance with end-to-end feature extraction and classification in cutting-edge image analysis-based computer-aided-diagnosis (CAD) procedures. Automated malaria screening employing DL approaches could contribute in the development of an effective diagnostic aid. In this study, we assessed the efficacy of VGG16, EfficientNetB3, InceptionV3, and ResNet50 as feature extractors to categorise parasitized and uninfected cells and aid in enhanced malaria disease screening. Our results showed that optimum accuracy of 0.97 is achieved after 40 epochs. Our study demonstrated the successful application of deep learning techniques, specifically ResNet50 and EfficientNetB3, among the analysed models, for malaria disease screening and detection.
{"title":"Performance Analysis of Transfer Learning Methods for Malaria Disease Identification","authors":"E.S.K. Chandrasekara, S. Vidanagamachchi","doi":"10.1109/SCSE59836.2023.10214984","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10214984","url":null,"abstract":"Malaria has become a widespread disease and one of the leading causes of many deaths worldwide. Malaria is a blood disease brought on by Plasmodium parasites, which are transmitted by the bite of a female Anopheles mosquito. To diagnose the condition, medical experts analyse thick and thin blood smears. However, their precision is dependent on the quality of the smear and experience in categorising and counting parasitized and uninfected cells. Such an investigation could be complicated and time-consuming for large-scale diagnosis, resulting in poor quality as well. Deep learning (DL) approaches such as Convolutional Neural Networks (CNN) offer highly scalable and improved performance with end-to-end feature extraction and classification in cutting-edge image analysis-based computer-aided-diagnosis (CAD) procedures. Automated malaria screening employing DL approaches could contribute in the development of an effective diagnostic aid. In this study, we assessed the efficacy of VGG16, EfficientNetB3, InceptionV3, and ResNet50 as feature extractors to categorise parasitized and uninfected cells and aid in enhanced malaria disease screening. Our results showed that optimum accuracy of 0.97 is achieved after 40 epochs. Our study demonstrated the successful application of deep learning techniques, specifically ResNet50 and EfficientNetB3, among the analysed models, for malaria disease screening and detection.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129099364","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}
Pub Date : 2023-06-29DOI: 10.1109/scse59836.2023.10215032
{"title":"Copyright Page","authors":"","doi":"10.1109/scse59836.2023.10215032","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215032","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129154068","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}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215047
S.P.M. Abeywickrama, P. D. Dinesh Asanka
The electricity demand in Sri Lanka is expected to increase steadily over time. Planning for future demand and ensuring an adequate electricity supply poses a significant challenge. It is crucial to accurately forecast future demand in order to maintain an uninterrupted power supply. Previous studies have explored the correlation between weather factors and electricity demand with the aim of accurately predicting demand values. Thus, the objective of this study is to forecast the monthly electricity demand in Sri Lanka, by considering the influence of weather patterns. In this study, rainfall, humidity, and temperature weather parameters, along with historical monthly demand data, are taken into consideration. The identification of the most crucial weather variables is based on their correlation with electricity demand data. Various techniques have been employed for forecasting electricity demand over the past decade. However, the limitation of previous studies lies in their failure to incorporate past weather data alongside electricity demand data. This gap is addressed in the present study. This study used Vector Auto Regression (VAR) and Long Short-Term Memory (LSTM) models to forecast monthly electricity demand in each district of Sri Lanka. The VAR model demonstrated lower values by comparing the performance metrics, including Root Mean Square Error, Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. As a result, the VAR model was chosen as the most suitable model for forecasting monthly electricity demand by incorporating weather variables.
{"title":"Integrating Weather Patterns into Machine Learning Models for Improved Electricity Demand Forecasting in Sri Lanka","authors":"S.P.M. Abeywickrama, P. D. Dinesh Asanka","doi":"10.1109/SCSE59836.2023.10215047","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215047","url":null,"abstract":"The electricity demand in Sri Lanka is expected to increase steadily over time. Planning for future demand and ensuring an adequate electricity supply poses a significant challenge. It is crucial to accurately forecast future demand in order to maintain an uninterrupted power supply. Previous studies have explored the correlation between weather factors and electricity demand with the aim of accurately predicting demand values. Thus, the objective of this study is to forecast the monthly electricity demand in Sri Lanka, by considering the influence of weather patterns. In this study, rainfall, humidity, and temperature weather parameters, along with historical monthly demand data, are taken into consideration. The identification of the most crucial weather variables is based on their correlation with electricity demand data. Various techniques have been employed for forecasting electricity demand over the past decade. However, the limitation of previous studies lies in their failure to incorporate past weather data alongside electricity demand data. This gap is addressed in the present study. This study used Vector Auto Regression (VAR) and Long Short-Term Memory (LSTM) models to forecast monthly electricity demand in each district of Sri Lanka. The VAR model demonstrated lower values by comparing the performance metrics, including Root Mean Square Error, Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. As a result, the VAR model was chosen as the most suitable model for forecasting monthly electricity demand by incorporating weather variables.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129242454","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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