Short-term power load forecasting plays a crucial role in improving the operational efficiency and economic benefit of the state grid system. To enhance the power load forecasting performance, several models have been...
详细信息
Short-term power load forecasting plays a crucial role in improving the operational efficiency and economic benefit of the state grid system. To enhance the power load forecasting performance, several models have been proposed, and various combined models have shown good performance. Nevertheless, many current combined models neglect the importance and necessity of data pretreatment;in particular, they do not fully consider different noise patterns in different datasets. Moreover, they rarely consider deep learning in their model combinations. The current research gap limits the forecasting enhancement of the current combined model. In this paper, an ensemble power load forecasting system, which combines a competitive-inhibition feature selection strategy and a deep-learning-participating model, is proposed to address this gap. This ensemble forecasting system successfully improves the forecasting efficiency, and a case study with real 30-min power load data from four cities in Australia clearly demonstrates that the proposed system is significantly better than the comparison models. Therefore, the proposed system is a valid tool for smart grid planning.
Artificial Intelligence of Things (AIoT) is a new research area in AI and IoT. For the massive data and connections at the edge of AIoT, how to schedule the resource load is a key problem to be solved. For the multi-o...
详细信息
Artificial Intelligence of Things (AIoT) is a new research area in AI and IoT. For the massive data and connections at the edge of AIoT, how to schedule the resource load is a key problem to be solved. For the multi-objective resource scheduling problem in a cross-domain environment, this article proposes a resource scheduling method based on edge computing and multi-objectivealgorithms. First, a cloud-edge hybrid AIoT hierarchical network resource management architecture is constructed. Then, the resource scheduling problem is modeled and a linear weighting strategy is designed for search space "pruning", which is then optimized by three multi-objectivealgorithms. Finally, we conducted a series of experiments based on the iFogSim platform. The simulation results show that the proposed resource scheduling strategy can effectively reduce the processing delay of AIoT and effectively improve the energy utilization efficiency of devices in the network.
With the continuous increase in the number of vehicles and people on urban roads, various traffic problems have become increasingly serious and intelligent transportation has gradually gained widespread attention. As ...
详细信息
With the continuous increase in the number of vehicles and people on urban roads, various traffic problems have become increasingly serious and intelligent transportation has gradually gained widespread attention. As a computing paradigm that could effectively reduce the task processing time consumption of user device and the cost of cloud server, edge computing has become an indispensable part of intelligent transportation system. However, how to reduce the load imbalance of edge computing server while ensuring that the task processing of intelligent transportation device takes less time consumption and energy consumption has become a challenge. In order to tackle this challenge, the computation offloading decision-making problem in the intelligent transportation edge computing scenario was modeled as a multi-objectiveoptimization problem in this paper, and an adaptive multi-objective optimization algorithm (E-NSGA-III) based on NSGA-III was used to solve this problem, and comparative experiment with other methods was made. Experimental results show that compared with NSGA-II, MOEA/D and NSGA-III, proposed algorithm (E-NSGA-III) in this paper can mostly reduce time consumption by 14.28%, 18.42% and 9.82%, energy consumption by 5.59%, 6.79% and 4.83%, and load balancing variances by 21.73%, 33.46% and 18.25%.
Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features ...
详细信息
Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.
On-the-fly assembled multistage adaptive testing (OMST) is a recently emerging computer-administered test form and has been employed in a variety of large-scale examinations. Item selection is a key challenge in OMST,...
详细信息
ISBN:
(纸本)9783031366215;9783031366222
On-the-fly assembled multistage adaptive testing (OMST) is a recently emerging computer-administered test form and has been employed in a variety of large-scale examinations. Item selection is a key challenge in OMST, which adaptively assembles real-time question modules by selecting a set of questions tailored for examinees from item bank. Existing question module assembly recognizes the measurement efficiency, test diversity and security as crucial evaluation criteria for item selection. Nevertheless, most of the studies on OMST concentrate on improving the measurement efficiency in item selection, while few of them devote to making a trade-off between the three criteria. In this paper, we propose to use a dynamic indicator-based multi-objective evolutionary algorithm with reference point adaptation, termed AR-DMOEA, for striking a balance between the three criteria in the item selection of OMST. A diversity-strengthened population initialization strategy is suggested to generate diverse individuals in the initial iteration of AR-DMOEA. A set of knowledge-guided offspring generation operators are designed to reproduce high-quality offspring individuals during the optimization of AR-DMOEA. Empirical results on five OMST datasets demonstrate that AR-DMOEA effectively balances the measurement efficiency, test diversity and security in the item selection of OMST. The effectiveness of the suggested initialization strategy and offspring generation operators are also validated by comparison between AR-DMOEA with the two strategies and that without the two strategies.
By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices;it is done by spending a hug...
详细信息
By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices;it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objectiveoptimization problem with power consumption and resource wastage minimi-zation perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of JAYA (MOD-JAYA) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-JAYA algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics.
The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challeng...
详细信息
The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challenges in modeling its uncertainty;research in this field is limited. A quantile regression bi-directional long short-term memory network (QrBiLStm) and a novel ensemble probabilistic forecasting strategy are proposed in this study to explore ensemble probabilistic forecasting. To verify the reliability of the proposed ensemble probabilistic forecasting system, the uncertainties of wind speed at wind farms in China were modeled as a case study. The results of comparative experiments including 15 other models demonstrate the superiority of this ensemble probabilistic forecasting system in terms of sharpness while maintaining high interval coverage. More specifically, it was observed that the prediction interval coverage probability obtained by the proposed system is above 97%, and the sharpness is improved by at least 24.21% as compared with the commonly used single models. The proposed ensemble probabilistic forecasting system can accurately quantify the uncertainty of wind speed, and also reduce the operation cost of power systems by improving the efficiency of wind energy utilization.
In recent years, the use of Building Information Modeling (BIM) with Building Energy Modeling (BEM) has become the primary research focus for reducing the energy consumption of buildings in the planning and operationa...
详细信息
In recent years, the use of Building Information Modeling (BIM) with Building Energy Modeling (BEM) has become the primary research focus for reducing the energy consumption of buildings in the planning and operational phases. The combination of BIM and BEM offers advantages for the various phases of a construction project. However, there are currently very few studies that can integrate multi-objective optimization algorithms into the BIM-BEM process to achieve automatic optimization and effectively manage many aspects of building development. In this study, an EnergyPlus integrated multi-objective jellyfish search (EP-MOJSO) system was developed, utilizing an optimizationalgorithm to find the best thermal insulation layers for an Aluminum composite material (ACM) wall. The goal is to reduce the energy consumption and total cost in a BIM-BEM environment. In the case study, the authors successfully applied the system to a real building, resulting in a 10.7% reduction in total cost and a 65 kWh/m2/year reduction in EUI. It is expected that the results of the study will open up new ways of using algorithms for multi-criteria optimization in BIM models to optimize various project factors such as energy and total cost and thus make an important contribution to sustainable building design.
Stirling engines operate in a variety of temperatures and the electric power production via dish Stirling systems could be considered as an appropriate alternative for high-temperature solar concentrator energy harves...
详细信息
Stirling engines operate in a variety of temperatures and the electric power production via dish Stirling systems could be considered as an appropriate alternative for high-temperature solar concentrator energy harvesting systems. To this end, by performing various studies and analyses on the engine, Stirling cycle, and heat exchangers while utilizing the solar energy as the input thermal energy of the Stirling engine, parameters with the highest effect on the output power and engine stability are detected and considered as optimization variables. In this case, output power, thermal efficiency, and economic evaluation are taken to be the three suitable objective functions for multi-objectiveoptimization. Moreover, two optimizationalgorithms of MOPSO and SPEA/2 are introduced and applied for the first time for analyzing a dish Stirling engine. Finally, the optimization variable values before and after optimization, as well as the yielded improvement in the output power and thermal efficiency are presented, using LINAMP and TOPSIS techniques for optimum point selection. The optimization results show that the optimum conditions for three objective functions in case of three-objectiveoptimization acquired by LINAMP and TOPSIS methods are 38.96, 0.2391, 0.3127 and 38.71, 0.2433, 0.3152 of P-eta-F, respectively, acquired from optimization by SPEA/2 algorithm. However, single-objectiveoptimization of each of the three objective functions optimized separately yields the values of (42.27, 27.51, and 0.3379) and (42.12, 27.62, and 0.3361) for MOPSO and SPEA/2 algorithms, respectively.
In this paper, due to the improper selection of process parameters in curved surface milling, the tool wear is accelerated, and the surface quality and dimensional accuracy of parts are difficult to control. Using BBD...
详细信息
In this paper, due to the improper selection of process parameters in curved surface milling, the tool wear is accelerated, and the surface quality and dimensional accuracy of parts are difficult to control. Using BBD orthogonal test and extreme difference analysis method, based on the basic theory of thermo-mechanical coupling elastic-plastic mechanics, the application of Deform-3D finite element simulation software to complete the simulation of blade milling machining simulation can be obtained tool wear and the rule of change of milling temperature. Analysis of variance was used to test the degree of fit between the mathematical model and test value (P<0.0001). Taking tool wear and material removal rate as optimizationobjectives and spindle speed, feed per tooth, and milling depth as constraints, a multivariate quadratic regression model of process parameters was established. The optimum combination of process parameters was obtained by the multi-objectiveoptimization method. The results show that, without considering other conditions, effective machining parameters combination can be obtained through the multi-objectiveoptimization method, achieving control tool wear and improving material removal rate.
暂无评论