67 research outputs found
Mining Software Architecture Knowledge: Classifying Stack Overflow Posts Using Machine Learning
No full textSoftware Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions. The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning‐based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K‐Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni‐gram feature set provides best classification results and attains 85.80% accuracy. For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni‐gram feature set proved to be the most suitable
Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan
No full textFloods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach
An integrated model with interdependent water storage for optimal resource management in Energy-Water-Food Nexus
Get PDFWith the rapid increase in population and the industrial revolution, the demand for clean energy and water has substantially increased, underscoring their importance for sustainable economic development. Although energy and water infrastructures are often viewed as separate and uncoupled due to distinct processes in power generation and water production, they are fundamentally interlinked within their respective domains. This necessitates a strong coupling to optimally manage power and water resources simultaneously. To address this, a joint optimization algorithm has been developed to manage the supply-side resources of the Energy-Water-Food Nexus (EWFN), including the power, water, food, cogeneration, and storage networks. A mathematical model is first developed to dispatch clear power, potable water, and storage resources, considering constraints related to supply, demand, production, flow, and ramping. Additionally, the integration of a water storage facility alleviates binding constraints, enabling flat production to reduce costs and
emissions. The proposed methodology also allows for the real-time quantification of production costs, energy mix, reserve and curtailed capacities, and energy imbalances. This methodological extension to EWFN includes flexible resources within the grid’s portfolio to promote cleaner production, ensuring that the required amount of water is consumed across all sectors. Finally, the proposed algorithm is tested on freely available datasets, demonstrating that the co-dispatch of energy and water resources in the presence of constraints leads to optimal generation and distribution of power and water without heavily relying on a single-product plant
Optimal Placement of Capacitors in Radial Distribution Grids via Enhanced Modified Particle Swarm Optimization
No full textThis paper presents the integration of shunt capacitors in the radial distribution grids (RDG) with constant and time-varying load consideration for the reduction of power losses and total annual cost, which turns to enhance the voltage profile and annual net savings. To gather the stated goals, three objective functions are formulated with system constraints. To solve this identified problem, a novel optimization technique based on the modification of particle swarm optimization is proposed. The solution methodology is divided into two phases. In phase one, potential candidate buses are nominated using the loss sensitivity factor method and in phase two the proposed technique first selects the optimal buses for the capacitor placement among the potential buses then it decides the optimal sizing of the capacitors as well. To demonstrate the performance in terms of efficiency and strength, the proposed technique is tested on IEEE 15, 33, and 69 bus system for the optimal placement and sizing of capacitors (OPSC) problem. The results are achieved in terms of annual net savings for 15 bus (47.66%case−1, 32.76%case−2, 26.46%case−3), 33 bus (33.09% case−1, 27.06%case−2, 24.15%case−3), and 69 bus (34.51% case−1, 29.43%case−2, 25.83%case−3) which are comparable to other state of the art methods, and it also indicates the success of the proposed technique
Co-optimization of energy and reserve capacity considering renewable energy unit with uncertainty
Get PDFThis paper proposes a system model for optimal dispatch of the energy and reserve capacity
considering uncertain load demand and unsteady power generation. This implicates uncertainty
in managing the power demand along with the consideration of utility, user and environmental
objectives. The model takes into consideration a day-ahead electricity market that involves the
varying power demand bids and generates a required amount of energy in addition with reserve
capacity. The lost opportunity cost is also considered and incorporated within the context of expected
load not served. Then, the effects of combined and separate dispatching the energy and reserve are
investigated. The nonlinear cost curves have been addressed by optimizing the objective function
using robust optimization technique. Finally, various cases in accordance with underlying parameters
have been considered in order to conduct and evaluate numerical results. Simulation results show
the effectiveness of proposed scheduling model in terms of reduced cost and system stability
Artificial intelligence-enabled probabilistic load demand scheduling with dynamic pricing involving renewable resource
Get PDFResidential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal
Community pharmacist's knowledge, attitude, roles and practices towards patient-centred care in Saudi Arabia: a systematic review of the literature:A Systematic Review of the Literature
Get PDFObjectives: This study aimed to evaluate published original studies in Saudi Arabia about knowledge, attitude, roles and practices of community pharmacists in providing patient-centred care services. Methods: Systematic searching of original studies published between 1 January 2007 and 31 December 2017 using electronic databases: PubMed, International Pharmaceutical Abstracts, Scopus, Science Direct, Cochrane Library, TRiP database, Springer Link and Google Scholar. Studies were included if they outlined community pharmacist's knowledge, role, attitude and professional practice behaviours towards patient-centred care provided by pharmacists alone or in collaboration with other healthcare professional (s). The studies were identified, and data were extracted independently by two reviewers. The modified Newcastle-Ottawa scale for cross-sectional studies was used to assess the quality of each study. Key findings: Twenty-four original studies conducted in Saudi Arabia were included. Majority of studies were questionnaire-based surveys (62.5%). One quarter of the studies investigated knowledge, roles and attitude of community pharmacists about irrational dispensing and prescribing of antibiotics and prescription only medicines. Included studies highlighted numerous gaps in knowledge, attitude, roles and practices of community pharmacists in Saudi Arabia in providing efficient patient-centred care services. Lack of knowledge and time, absence of pharmacy information database, deficiency of continued professional development training, unavailability of adverse drug reaction reporting forms and professional and cultural issues were some of the barriers in providing patient-centred care. Conclusions: The studies showed that although community pharmacists in Saudi Arabia do provide medicine counselling and other patient-centred care services; however, these services need substantial improvement. This review may be useful for policy makers, regulators, pharmacy educators and researchers in understanding the work being performed in the community pharmacy setting in Saudi Arabia.</p
An optimization cost strategy for storage-enabled hydrogen flow network using Monte Carlo simulation
Get PDFThis article presents an innovative approach to address the optimization and planning of hydrogen network transmission, focusing on minimizing computational and operational costs, including capital, operational, and maintenance expenses. The mathematical models developed for gas flow rate, pipelines, junctions, and storage form the basis for the optimization problem, which aims to reduce costs while satisfying equality, inequality, and binary constraints. To achieve this, we implement a dynamic algorithm incorporating 100 scenarios to account for uncertainty. Unlike conventional successive linear programming methods, our approach solves successive piecewise problems and allows comparisons with other techniques, including stochastic and deterministic methods. Our method significantly reduces computational time (56 iterations) compared to deterministic (92 iterations) and stochastic (77 iterations) methods. The non-convex nature of the model necessitates careful selection of starting points to avoid local optimal solutions, which is addressed by transforming the primal problem into a linear program by fixing the integer variable. The LP problem is then efficiently solved using the Complex Linear Programming Expert (CPLEX) solver, enhanced by Monte Carlo simulations for 100 scenarios, achieving a 39.13% reduction in computational time. In addition to computational efficiency, this approach leads to operational cost savings of 25.02% by optimizing the selection of compressors (42.8571% decreased) and storage facilities. The model?s practicality is validated through real-world simulations on the Belgian gas network, demonstrating its potential in solving large-scale hydrogen network transmission planning and optimization challenges.This article presents an innovative approach to address the optimization and planning of hydrogen network transmission, focusing on minimizing computational and operational costs, including capital, operational, and maintenance expenses. The mathematical models developed for gas flow rate, pipelines, junctions, and storage form the basis for the optimization problem, which aims to reduce costs while satisfying equality, inequality, and binary constraints. To achieve this, we implement a dynamic algorithm incorporating 100 scenarios to account for uncertainty. Unlike conventional successive linear programming methods, our approach solves successive piecewise problems and allows comparisons with other techniques, including stochastic and deterministic methods. Our method significantly reduces computational time (56 iterations) compared to deterministic (92 iterations) and stochastic (77 iterations) methods. The non-convex nature of the model necessitates careful selection of starting points to avoid local optimal solutions, which is addressed by transforming the primal problem into a linear program by fixing the integer variable. The LP problem is then efficiently solved using the Complex Linear Programming Expert (CPLEX) solver, enhanced by Monte Carlo simulations for 100 scenarios, achieving a 39.13% reduction in computational time. In addition to computational efficiency, this approach leads to operational cost savings of 25.02% by optimizing the selection of compressors (42.8571% decreased) and storage facilities. The model?s practicality is validated through real-world simulations on the Belgian gas network, demonstrating its potential in solving large-scale hydrogen network transmission planning and optimization challenges
A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid
No full textThe smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%
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