The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID) controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.
{"title":"A mMSA-FOFPID controller for AGC of multi-area power system with multi-type generations","authors":"Dillip Khamari , Rabindra Kumar Sahu , Sidhartha Panda , Yogendra Arya","doi":"10.1016/j.suscom.2024.101046","DOIUrl":"10.1016/j.suscom.2024.101046","url":null,"abstract":"<div><div>The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID) controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101046"},"PeriodicalIF":3.8,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.suscom.2024.101045
Mohamed Abdel-Basset , Reda Mohamed , Doaa El-Shahat , Karam M. Sallam , Ibrahim M. Hezam , Nabil M. AbdelAziz
The mobile edge computing system supported by multiple unmanned aerial vehicles (UAVs) has gained significant interest over the last few decades due to its flexibility and ability to enhance communication coverage. In this system, the UAVs function as edge servers to offer computing services to Internet of Things devices (IoTDs), and if they are located distant from those devices, a significant amount of energy is consumed while data is transmitted. Therefore, optimizing UAVs’ trajectories is an indispensable process to minimize overall energy consumption in this system. This problem is difficult to solve because it requires multiple considerations, including the number and placement of stop points (SPs), their order, and the association between SPs and UAVs. A few studies in the literature have been presented to address all of these aspects; nevertheless, the majority of them suffer from slow convergence speed, stagnation in local optima, and expensive computational costs. Therefore, this study presents a new trajectory optimization algorithm, namely ITPA-GBOKM, based on a newly proposed transfer-based encoding mechanism, gradient-based optimizer, and K-Medoids Clustering algorithm to tackle this problem more accurately. The K-medoid clustering algorithm is used to achieve better association between UAVs and SPs since it is less sensitive to outliers than the K-means clustering algorithm; the transfer function-based encoding mechanism is used to efficiently define this problem’s solutions and manage the number of SPs; and GBO is utilized to search for the best SPs that could minimize overall energy consumption, including that consumed by UAVs and IoTDs. The proposed ITPA-GBOKM is evaluated using 13 instances with several IoTDs ranging from 60 to 700 to show its effectiveness in dealing with the trajectory optimization problem at small, medium, and large scales. Furthermore, it is compared to several rival optimizers using a variety of performance metrics, including average fitness, multiple comparison test, Wilcoxon rank sum test, standard deviation, Friedman mean rank, and convergence speed, to show its superiority. The experimental results indicates that this algorithm is capable of producing significantly different and superior results compared to the rival algorithms.
{"title":"Energy-efficient trajectory optimization algorithm based on K-medoids clustering and gradient-based optimizer for multi-UAV-assisted mobile edge computing systems","authors":"Mohamed Abdel-Basset , Reda Mohamed , Doaa El-Shahat , Karam M. Sallam , Ibrahim M. Hezam , Nabil M. AbdelAziz","doi":"10.1016/j.suscom.2024.101045","DOIUrl":"10.1016/j.suscom.2024.101045","url":null,"abstract":"<div><div>The mobile edge computing system supported by multiple unmanned aerial vehicles (UAVs) has gained significant interest over the last few decades due to its flexibility and ability to enhance communication coverage. In this system, the UAVs function as edge servers to offer computing services to Internet of Things devices (IoTDs), and if they are located distant from those devices, a significant amount of energy is consumed while data is transmitted. Therefore, optimizing UAVs’ trajectories is an indispensable process to minimize overall energy consumption in this system. This problem is difficult to solve because it requires multiple considerations, including the number and placement of stop points (SPs), their order, and the association between SPs and UAVs. A few studies in the literature have been presented to address all of these aspects; nevertheless, the majority of them suffer from slow convergence speed, stagnation in local optima, and expensive computational costs. Therefore, this study presents a new trajectory optimization algorithm, namely ITPA-GBOKM, based on a newly proposed transfer-based encoding mechanism, gradient-based optimizer, and K-Medoids Clustering algorithm to tackle this problem more accurately. The K-medoid clustering algorithm is used to achieve better association between UAVs and SPs since it is less sensitive to outliers than the K-means clustering algorithm; the transfer function-based encoding mechanism is used to efficiently define this problem’s solutions and manage the number of SPs; and GBO is utilized to search for the best SPs that could minimize overall energy consumption, including that consumed by UAVs and IoTDs. The proposed ITPA-GBOKM is evaluated using 13 instances with several IoTDs ranging from 60 to 700 to show its effectiveness in dealing with the trajectory optimization problem at small, medium, and large scales. Furthermore, it is compared to several rival optimizers using a variety of performance metrics, including average fitness, multiple comparison test, Wilcoxon rank sum test, standard deviation, Friedman mean rank, and convergence speed, to show its superiority. The experimental results indicates that this algorithm is capable of producing significantly different and superior results compared to the rival algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101045"},"PeriodicalIF":3.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.suscom.2024.101044
B. Swathi , Dr. M. Amanullah , S.A. Kalaiselvan
Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant; it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.
{"title":"Energy-efficient and fault-tolerant routing mechanism for WSN using optimizer based deep learning model","authors":"B. Swathi , Dr. M. Amanullah , S.A. Kalaiselvan","doi":"10.1016/j.suscom.2024.101044","DOIUrl":"10.1016/j.suscom.2024.101044","url":null,"abstract":"<div><div>Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant; it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101044"},"PeriodicalIF":3.8,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.suscom.2024.101043
R.M. Bhavadharini , Suseela Sellamuthu , G. Sudhakaran , Ahmed A. Elngar
Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.
{"title":"Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network","authors":"R.M. Bhavadharini , Suseela Sellamuthu , G. Sudhakaran , Ahmed A. Elngar","doi":"10.1016/j.suscom.2024.101043","DOIUrl":"10.1016/j.suscom.2024.101043","url":null,"abstract":"<div><div>Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101043"},"PeriodicalIF":3.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.suscom.2024.101042
Dinesh Kumar Jayaraman Rajanediran , C. Ganesh Babu , K. Priyadharsini
Acceleration techniques play a crucial role in enhancing the performance of modern high-speed computations, especially in Deep Learning (DL) applications where the speed is of utmost importance. One essential component in this context is the Systolic Array (SA), which effectively handles computational tasks and data processing in a rhythmic manner. Google's Tensor Processing Unit (TPU) leverages the power of SA for neural networks. The core SA's functionality and performance lies in the Computation Element (CE), which facilitates parallel data flow. In our article, we introduce a novel approach called Proposed Systolic Array (PSA), which is implemented on the CE and further enhanced with a modified Hybrid Kogge Stone adder (MHA). This design incorporates principles to expedite computations by rounding and extracting data model in SA as PSA-MHA. The PSA, utilizing a data flow model with MHA, significantly accelerates data shifts and control passes in execution cycles. We validated our approach through simulations on the Cadence Virtuoso platform using 65 nm process technology, comparing it to the General Matrix Multiplication (GMMN) benchmark. The results showed remarkable improvements in the CE, with a 30.29 % reduction in delay, a 23.07 % reduction in area, and an 11.87 % reduction in power consumption. The PSA outperformed these improvements, achieving a 46.38 % reduction in delay, a 7.58 % reduction in area, and an impressive 48.23 % decrease in Area Delay Product (ADP). To further substantiate our findings, we applied the PSA-based approach to pre-trained hybrid Convolutional and Recurrent (CNN-RNN) neural models. The PSA-based hybrid model incorporates 189 million Multiply-Accumulate (MAC) units, resulting in a weighted mean architecture value of 784.80 for the RNN component. We also explored variations in bit width, which led to delay reductions ranging from 20.17 % to 30.29 %, area variations between 13.08 % and 32.16 %, and power consumption changes spanning from 11.88 % to 20.42 %.
{"title":"A certain examination on heterogeneous systolic array (HSA) design for deep learning accelerations with low power computations","authors":"Dinesh Kumar Jayaraman Rajanediran , C. Ganesh Babu , K. Priyadharsini","doi":"10.1016/j.suscom.2024.101042","DOIUrl":"10.1016/j.suscom.2024.101042","url":null,"abstract":"<div><div>Acceleration techniques play a crucial role in enhancing the performance of modern high-speed computations, especially in Deep Learning (DL) applications where the speed is of utmost importance. One essential component in this context is the Systolic Array (SA), which effectively handles computational tasks and data processing in a rhythmic manner. Google's Tensor Processing Unit (TPU) leverages the power of SA for neural networks. The core SA's functionality and performance lies in the Computation Element (CE), which facilitates parallel data flow. In our article, we introduce a novel approach called Proposed Systolic Array (PSA), which is implemented on the CE and further enhanced with a modified Hybrid Kogge Stone adder (MHA). This design incorporates principles to expedite computations by rounding and extracting data model in SA as PSA-MHA. The PSA, utilizing a data flow model with MHA, significantly accelerates data shifts and control passes in execution cycles. We validated our approach through simulations on the Cadence Virtuoso platform using 65 nm process technology, comparing it to the General Matrix Multiplication (GMMN) benchmark. The results showed remarkable improvements in the CE, with a 30.29 % reduction in delay, a 23.07 % reduction in area, and an 11.87 % reduction in power consumption. The PSA outperformed these improvements, achieving a 46.38 % reduction in delay, a 7.58 % reduction in area, and an impressive 48.23 % decrease in Area Delay Product (ADP). To further substantiate our findings, we applied the PSA-based approach to pre-trained hybrid Convolutional and Recurrent (CNN-RNN) neural models. The PSA-based hybrid model incorporates 189 million Multiply-Accumulate (MAC) units, resulting in a weighted mean architecture value of 784.80 for the RNN component. We also explored variations in bit width, which led to delay reductions ranging from 20.17 % to 30.29 %, area variations between 13.08 % and 32.16 %, and power consumption changes spanning from 11.88 % to 20.42 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101042"},"PeriodicalIF":3.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.suscom.2024.101041
Rahul Gupta , Aseem Chandel
The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.
{"title":"A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance","authors":"Rahul Gupta , Aseem Chandel","doi":"10.1016/j.suscom.2024.101041","DOIUrl":"10.1016/j.suscom.2024.101041","url":null,"abstract":"<div><div>The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101041"},"PeriodicalIF":3.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.suscom.2024.101040
Bassam A. Abdelghani, Ahlam Al Mohammad, Jamal Dari, Mina Maleki, Shadi Banitaan
Occupancy prediction has been the subject of ongoing research, employing various methods and data sources to improve occupancy prediction accuracy and energy efficiency in buildings. Precise occupancy prediction is crucial for optimizing energy usage, ensuring occupant comfort, and enhancing building management. With the increasing demand for intelligent building management systems, robust and accurate occupancy prediction models are becoming more critical. This study aims to predict building occupancy using WiFi Syslog files from three different datasets: an open-source dataset from the University of Massachusetts Dartmouth, a new locally collected dataset from the dental school at the University of Detroit Mercy, and finally, a dataset from an office building in Berkeley, California. Two types of features, static features, and MOTIF time series features, were extracted from the datasets to process and compare their performance in occupancy prediction.
The first step of the proposed framework consisted of selecting the most suitable time range to compare occupancy prediction models between different datasets. It was concluded that this analysis was best conducted semester by semester. Multiple regression algorithms, such as random forest and LightGBM, were applied in the following step, along with advanced ensemble techniques, including stacking and blending, to assess the model. The stacking regression showed the best results for static features across all datasets. It achieved a Coefficient of Determination () of 0.9540 in the first dataset, 0.9482 in the second, and 0.9977 in the third. For MOTIF features, however, the best algorithm depended on the dataset. All algorithms performed similarly in the first dataset, with of 0.956. In contrast, LightGBM and the Stacking Regressor had better results than the others in the second dataset, with a low of 0.531 due to dataset-specific differences. The stacking regression once again delivered the best results in the last dataset with an of 0.9967.
{"title":"Occupancy prediction: A comparative study of static and MOTIF time series features using WiFi Syslog data","authors":"Bassam A. Abdelghani, Ahlam Al Mohammad, Jamal Dari, Mina Maleki, Shadi Banitaan","doi":"10.1016/j.suscom.2024.101040","DOIUrl":"10.1016/j.suscom.2024.101040","url":null,"abstract":"<div><div>Occupancy prediction has been the subject of ongoing research, employing various methods and data sources to improve occupancy prediction accuracy and energy efficiency in buildings. Precise occupancy prediction is crucial for optimizing energy usage, ensuring occupant comfort, and enhancing building management. With the increasing demand for intelligent building management systems, robust and accurate occupancy prediction models are becoming more critical. This study aims to predict building occupancy using WiFi Syslog files from three different datasets: an open-source dataset from the University of Massachusetts Dartmouth, a new locally collected dataset from the dental school at the University of Detroit Mercy, and finally, a dataset from an office building in Berkeley, California. Two types of features, static features, and MOTIF time series features, were extracted from the datasets to process and compare their performance in occupancy prediction.</div><div>The first step of the proposed framework consisted of selecting the most suitable time range to compare occupancy prediction models between different datasets. It was concluded that this analysis was best conducted semester by semester. Multiple regression algorithms, such as random forest and LightGBM, were applied in the following step, along with advanced ensemble techniques, including stacking and blending, to assess the model. The stacking regression showed the best results for static features across all datasets. It achieved a Coefficient of Determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.9540 in the first dataset, 0.9482 in the second, and 0.9977 in the third. For MOTIF features, however, the best algorithm depended on the dataset. All algorithms performed similarly in the first dataset, with <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.956. In contrast, LightGBM and the Stacking Regressor had better results than the others in the second dataset, with a low <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.531 due to dataset-specific differences. The stacking regression once again delivered the best results in the last dataset with an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.9967.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101040"},"PeriodicalIF":3.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1016/j.suscom.2024.101039
Fangcheng Guo , Jingjin Li , Chung Ket Thein , Anqi Gao , Jianfeng Ren , Chang Heon Lee , Jiawei Li , Tianxiang Cui , Heng Yu
The rising demand for renewable energy supply in standalone computing devices has led to the emergence of vibration energy harvesting (VEH) to overcome technical and environmental challenges. For instance, VEH is desirable in IoT scenarios where maintaining a battery supply is non-sustainable or impractical due to many devices or remote circumstances. VEH can be environmentally friendly given that it reduces the reliance on traditional battery production and usage, thus reducing the carbon footprint and chemical waste in disposable batteries. However, a significant hurdle in VEH adoption is the lack of effective simulation tools for generating various application scenarios to describe, validate, or predict the efficacy of the VEH-based devices. It is necessary for designing and implementing a VEH simulator for a variety of realistic application scenarios. Being the first of its kind, this study presents a scenario-customizable and visual-rendering VEH simulation system based on the Unity3D Engine. The proposed simulator features a modular design that consists of several key functional components including vibration scenarios’ creation and manipulation, VEH model specification, Unity-Python Co-computing mechanism, and 3D visualization. This paper also presents two AI-based case studies leveraging the functionality and data provided by the simulator to demonstrate its potential for data-driven research and applications.
{"title":"A scenario-customizable and visual-rendering simulator for on-vehicle vibration energy harvesting","authors":"Fangcheng Guo , Jingjin Li , Chung Ket Thein , Anqi Gao , Jianfeng Ren , Chang Heon Lee , Jiawei Li , Tianxiang Cui , Heng Yu","doi":"10.1016/j.suscom.2024.101039","DOIUrl":"10.1016/j.suscom.2024.101039","url":null,"abstract":"<div><div>The rising demand for renewable energy supply in standalone computing devices has led to the emergence of vibration energy harvesting (VEH) to overcome technical and environmental challenges. For instance, VEH is desirable in IoT scenarios where maintaining a battery supply is non-sustainable or impractical due to many devices or remote circumstances. VEH can be environmentally friendly given that it reduces the reliance on traditional battery production and usage, thus reducing the carbon footprint and chemical waste in disposable batteries. However, a significant hurdle in VEH adoption is the lack of effective simulation tools for generating various application scenarios to describe, validate, or predict the efficacy of the VEH-based devices. It is necessary for designing and implementing a VEH simulator for a variety of realistic application scenarios. Being the first of its kind, this study presents a scenario-customizable and visual-rendering VEH simulation system based on the Unity3D Engine. The proposed simulator features a modular design that consists of several key functional components including vibration scenarios’ creation and manipulation, VEH model specification, Unity-Python Co-computing mechanism, and 3D visualization. This paper also presents two AI-based case studies leveraging the functionality and data provided by the simulator to demonstrate its potential for data-driven research and applications.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101039"},"PeriodicalIF":3.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1016/j.suscom.2024.101038
Ricardo Sobjak , Eduardo Godoy de Souza , Claudio Leones Bazzi , Kelyn Schenatto , Nelson Miguel Betzek , Alan Gavioli
Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.
{"title":"Incorporation of computational routines in a microservice architecture in AgDataBox platform","authors":"Ricardo Sobjak , Eduardo Godoy de Souza , Claudio Leones Bazzi , Kelyn Schenatto , Nelson Miguel Betzek , Alan Gavioli","doi":"10.1016/j.suscom.2024.101038","DOIUrl":"10.1016/j.suscom.2024.101038","url":null,"abstract":"<div><div>Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101038"},"PeriodicalIF":3.8,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The optimal operation of the reservoir has vital importance in water engineering. In the presented article, a new optimization method, named sine cosine algorithm (SCA) was employed to obtain operating policy for an irrigation system. The SCA was utilized for the monthly operation of the Boukerdane Dam placed in the north of Algeria. The fitness function was the minimization of the total shortage for the studied period. Three scenarios considering three different seasons of inflow (dry, normal and wet) are used to optimize the reservoir system’s operation. The SCA outputs were compared with particle swarm optimization (PSO) and kidney algorithm (KA). The outcomes indicated that the SCA surpassed the PSO and KA in convergence rate. The general results indicated the low speed of KA and PSO in achieving convergence. The results indicated that the highest RES (resiliency index), SUS (sustainability index) and REL (reliability index) achieved by the SCA were 65, 86 and 92 %, respectively. Comparing the third scenario with the first and second scenarios, it was observed that the third scenario (wet seasons) improved the results.
{"title":"Optimization of reservoir operation by sine cosine algorithm: A case of study in Algeria","authors":"Merouane Boudjerda , Bénina Touaibia , Mustapha Kamel Mihoubi , Ozgur Kisi , Mohammd Ehteram , Ahmed El-Shafie","doi":"10.1016/j.suscom.2024.101035","DOIUrl":"10.1016/j.suscom.2024.101035","url":null,"abstract":"<div><div>The optimal operation of the reservoir has vital importance in water engineering. In the presented article, a new optimization method, named sine cosine algorithm (SCA) was employed to obtain operating policy for an irrigation system. The SCA was utilized for the monthly operation of the Boukerdane Dam placed in the north of Algeria. The fitness function was the minimization of the total shortage for the studied period. Three scenarios considering three different seasons of inflow (dry, normal and wet) are used to optimize the reservoir system’s operation. The SCA outputs were compared with particle swarm optimization (PSO) and kidney algorithm (KA). The outcomes indicated that the SCA surpassed the PSO and KA in convergence rate. The general results indicated the low speed of KA and PSO in achieving convergence. The results indicated that the highest RES (resiliency index), SUS (sustainability index) and REL (reliability index) achieved by the SCA were 65, 86 and 92 %, respectively. Comparing the third scenario with the first and second scenarios, it was observed that the third scenario (wet seasons) improved the results.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"44 ","pages":"Article 101035"},"PeriodicalIF":3.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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