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A Data-driven variable reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 254-266页

作者： Yuzhou Zhou Qiaozhu Zhai Lei Wu Moammad Shahidehpour Systems Engineering Institute MOEKLINNS LabXi’an Jiaotong UniversityXi’an 710049China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenU.S. Electrical and Computer Engineering Department Illinois Institute of TechnologyChicagoU.S. IEEE

This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving TCUC.However,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation burden.The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances online.On/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes.Numerical tests illustrate the efficiency of the proposed approach.

关键词： Unit commitment accelerated algorithm data driven variable reduction

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Reducing deep learning network structure through variable reduction methods in crop modeling

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Artificial Intelligence in Agriculture 2021年第1期5卷 196-207页

作者： Babak Saravi A.Pouyan Nejadhashemi Prakash Jha Bo Tang Department of Biosystems and Agricultural Engineering Michigan State UniversityEast LansingMI 48824USA Department of Plant Soil and Microbial Sciences Michigan State UniversityEast LansingMI 48824USA Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification Kansas State UniversityManhattanKS 66506USA Department of Electrical and Computer Engineering Mississippi State UniversityMS 39762USA

Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.

关键词： Deep learning Artificial intelligent variable reduction Crop modeling Yield prediction Irrigation

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