Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand;this coordination depends on training and deploying control policies and optimiz...Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand;this coordination depends on training and deploying control policies and optimization routines,which in turn require predictive models that can be queried efficiently over large building clusters.However,conventional physics-based simulators are computationally prohibitive for large-scale control training,and simple data-driven surrogates often lack the generalization needed for heterogeneous clusters.This paper introduces ScaleONet,a deep operator network framework designed for scalable,control-oriented modeling of building-cluster thermal dynamics.ScaleONet leverages the DeepONet paradigm to decouple and share learning across buildings:an LSTM-based branch network encodes outdoor climate and individual HVAC control signals,while a multilayer perceptron(MLP)-based trunk network embeds prediction timestamps,enabling fast predictions for growing clusters with negligible extra cost for each additional building or timestep.To the authors’knowledge,this is the first operator-learning method tailored to indoor air temperature forecasting in heterogeneous building clusters.Validation on thirty Belgian buildings(GenkNet)simulated in Dymola shows that,although a non-operator-learning LSTM baseline slightly outperforms ScaleONet for single-building cases,its error grows monotonically with cluster size.In contrast,ScaleONet’s median per-building-per-day RMSE decreases from 0.59°C at three buildings to 0.53°C at ten and 0.47°C at thirty,compared to 0.95°C for the LSTM at thirty buildings-a 51%reduction in prediction error.Error analysis across envelope heat-loss coefficients(UAbuilding)further reveals that while the LSTM’s RMSE increases for high-𝑈𝐴structures,ScaleONet maintains uniformly low error.With millisecond-scale inference(approximately 4 ms per sample for thirty buildings),ScaleONet is well suited for large-scale reinforcement learning,receding-horizon optimization,and real-time model predictive control.展开更多
This paper presents a PFCVF (Power Factor Correction) rectifier that uses a variable frequency source for alternators for electric and hybrid vehicles application. In such application, the frequency of the signal in t...This paper presents a PFCVF (Power Factor Correction) rectifier that uses a variable frequency source for alternators for electric and hybrid vehicles application. In such application, the frequency of the signal in the alternator changes according to the vehicle speed, more over the loading effect on the alternator introduces harmonic currents and increases the alternator apparent power requirements. To overcome these problems and aiming more stability and better design of the alternator, a new third harmonic injection technique is proposed. This technique allows to preserve a good THD (Total Harmonic Distortion) of the input source at any frequency and to decrease losses in semiconductors switches, thereby allowing more stability and reducing the apparent power requirements. A comparative study between the standard and the new technique is made and highlights the effectiveness of the new design. A detailed analysis of the proposed topology is presented and simulations as well as experimental results are shown.展开更多
The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks i...The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation control.To effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control data.By detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and lengths.After analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection engine.Finally,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.展开更多
In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopm...In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopman-based models for a fuel cell stack,incorporating K-fold cross-validation,varying lifted dimensions,radial basis functions(RBFs),and prediction horizons;and(2)comparison of the performance of Koopman-based approach with a more traditional physics-based model.The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior,with an error of less than 3%.The proposed approach offers several advantages,including enhanced computational efficiency,reduced computational burden,and improved interpretability.This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.展开更多
基金supported by KU Leuven,Belgium through the TECHPED-C2 project(C24M/21/021)which investigates tech-nically feasible and effective solutions for Positive Energy Districts.Additional support was provided by the National University of Singa-pore,Singapore through the Start-Up Grant(A-0009876-00-00)the Ministry of Education,Singapore,under the Academic Research Fund Tier 1(A-8003235-00-00).
文摘Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand;this coordination depends on training and deploying control policies and optimization routines,which in turn require predictive models that can be queried efficiently over large building clusters.However,conventional physics-based simulators are computationally prohibitive for large-scale control training,and simple data-driven surrogates often lack the generalization needed for heterogeneous clusters.This paper introduces ScaleONet,a deep operator network framework designed for scalable,control-oriented modeling of building-cluster thermal dynamics.ScaleONet leverages the DeepONet paradigm to decouple and share learning across buildings:an LSTM-based branch network encodes outdoor climate and individual HVAC control signals,while a multilayer perceptron(MLP)-based trunk network embeds prediction timestamps,enabling fast predictions for growing clusters with negligible extra cost for each additional building or timestep.To the authors’knowledge,this is the first operator-learning method tailored to indoor air temperature forecasting in heterogeneous building clusters.Validation on thirty Belgian buildings(GenkNet)simulated in Dymola shows that,although a non-operator-learning LSTM baseline slightly outperforms ScaleONet for single-building cases,its error grows monotonically with cluster size.In contrast,ScaleONet’s median per-building-per-day RMSE decreases from 0.59°C at three buildings to 0.53°C at ten and 0.47°C at thirty,compared to 0.95°C for the LSTM at thirty buildings-a 51%reduction in prediction error.Error analysis across envelope heat-loss coefficients(UAbuilding)further reveals that while the LSTM’s RMSE increases for high-𝑈𝐴structures,ScaleONet maintains uniformly low error.With millisecond-scale inference(approximately 4 ms per sample for thirty buildings),ScaleONet is well suited for large-scale reinforcement learning,receding-horizon optimization,and real-time model predictive control.
文摘This paper presents a PFCVF (Power Factor Correction) rectifier that uses a variable frequency source for alternators for electric and hybrid vehicles application. In such application, the frequency of the signal in the alternator changes according to the vehicle speed, more over the loading effect on the alternator introduces harmonic currents and increases the alternator apparent power requirements. To overcome these problems and aiming more stability and better design of the alternator, a new third harmonic injection technique is proposed. This technique allows to preserve a good THD (Total Harmonic Distortion) of the input source at any frequency and to decrease losses in semiconductors switches, thereby allowing more stability and reducing the apparent power requirements. A comparative study between the standard and the new technique is made and highlights the effectiveness of the new design. A detailed analysis of the proposed topology is presented and simulations as well as experimental results are shown.
基金supported by the Hainan Provincial Natural Science Foundation of China(Grant No.620RC562)the Liaoning Provincial Natural Science Foundation:Industrial Internet Identification Data Association Analysis Based on Machine Online Learning(Grant No.2022-KF-12-11)the Scientific Research Project of Educational Department of Liaoning Province(Grant No.LJKZ0082).
文摘The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation control.To effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control data.By detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and lengths.After analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection engine.Finally,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.
基金This material is based upon work supported by the National Science Foundation,United States under Grant No.2135735.
文摘In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopman-based models for a fuel cell stack,incorporating K-fold cross-validation,varying lifted dimensions,radial basis functions(RBFs),and prediction horizons;and(2)comparison of the performance of Koopman-based approach with a more traditional physics-based model.The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior,with an error of less than 3%.The proposed approach offers several advantages,including enhanced computational efficiency,reduced computational burden,and improved interpretability.This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.
