This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo,utilizing the region’s significant solar energy potential and high irradiation...This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo,utilizing the region’s significant solar energy potential and high irradiation levels.This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization(PSO)and grey wolf optimization(GWO)algorithms to maximize energy output,reduce levelised energy costs,and maximally reduce carbon emissions.Results show Tara-kan as the most optimal location,generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO_(2)e,while Samarinda is the least optimal location.Economically,electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value(NPV)with a payback period(PP)of 9-12 years,while a tariffof IDR 1,500/kWh is considered unfavorable.Thefindings demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project’sfinancial viability,with a positive NPV and reasonable PP.Implementing renewable energy systems in Kalimantan Island can improve energy effi-ciency and significantly reduce carbon emissions,supporting environmental sustainability goals.展开更多
An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Techniqu...An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared to the original data and traditional SMOTE. The proposed method (NR-Clustering SMOTE) improves accuracy by 15.34% on the Pima dataset and 20.96% on the Haberman dataset compared to SMOTE-LOF. Compared to Radius-SMOTE, this method increases accuracy by 3.16% on the Pima dataset and 13.24% on the Haberman dataset. Meanwhile, compared to RN-SMOTE, the accuracy improvement reaches 15.56% on the Pima dataset and 19.84% on the Haberman dataset. This research result implies that the proposed method experiences consistent performance improvement compared to traditional SMOTE and its latest variants, such as SMOTE-LOF, Radius-SMOTE, and RN-SMOTE, in solving imbalanced health data with class binaries.展开更多
基金supported by non-APBN UM 2024,Indonesia,with contract number 5.4.111/UN32.14.1/LT/2024.
文摘This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo,utilizing the region’s significant solar energy potential and high irradiation levels.This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization(PSO)and grey wolf optimization(GWO)algorithms to maximize energy output,reduce levelised energy costs,and maximally reduce carbon emissions.Results show Tara-kan as the most optimal location,generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO_(2)e,while Samarinda is the least optimal location.Economically,electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value(NPV)with a payback period(PP)of 9-12 years,while a tariffof IDR 1,500/kWh is considered unfavorable.Thefindings demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project’sfinancial viability,with a positive NPV and reasonable PP.Implementing renewable energy systems in Kalimantan Island can improve energy effi-ciency and significantly reduce carbon emissions,supporting environmental sustainability goals.
基金funded by Universitas Negeri Malang,contract number 4.4.841/UN32.14.1/LT/2024.
文摘An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared to the original data and traditional SMOTE. The proposed method (NR-Clustering SMOTE) improves accuracy by 15.34% on the Pima dataset and 20.96% on the Haberman dataset compared to SMOTE-LOF. Compared to Radius-SMOTE, this method increases accuracy by 3.16% on the Pima dataset and 13.24% on the Haberman dataset. Meanwhile, compared to RN-SMOTE, the accuracy improvement reaches 15.56% on the Pima dataset and 19.84% on the Haberman dataset. This research result implies that the proposed method experiences consistent performance improvement compared to traditional SMOTE and its latest variants, such as SMOTE-LOF, Radius-SMOTE, and RN-SMOTE, in solving imbalanced health data with class binaries.
