A Supervised-Learning-Based Strategy for Optimal Demand Response of an HVAC System in a Multi-Zone Office Building
暂无分享,去创建一个
[1] Heiko Wersing,et al. Incremental on-line learning: A review and comparison of state of the art algorithms , 2018, Neurocomputing.
[2] Amir Safdarian,et al. Integration of Price-Based Demand Response in DisCos' Short-Term Decision Model , 2014, IEEE Transactions on Smart Grid.
[3] Leslie K. Norford,et al. Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation , 2017 .
[4] Scott Sanner,et al. Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming , 2017, IJCAI.
[5] Venkata Sesha Praneeth Aketi. Prices in Wholesale Electricity Markets and Demand Response , 2014 .
[6] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[7] J. Kurnitski,et al. Data for occupancy internal heat gain calculation in main building categories , 2017, Data in brief.
[8] Leo Liberti,et al. Optimal HVAC Control as Demand Response with On-site Energy Storage and Generation System , 2015 .
[9] Munindar P. Singh,et al. Weather Forecasting Model using Artificial Neural Network , 2012 .
[10] Indra Riyanto,et al. Motion Sensor Application on Building Lighting Installation for Energy Saving and Carbon Reduction Joint Crediting Mechanism , 2018, Applied System Innovation.
[11] Lingling Fan,et al. Planning Energy Storage and Photovoltaic Panels for Demand Response With Heating Ventilation and Air Conditioning Systems , 2018, IEEE Transactions on Industrial Informatics.
[12] Yuanyuan Shi,et al. Optimal Control Via Neural Networks: A Convex Approach , 2018, ICLR.
[13] Young-Jin Kim,et al. Modeling and Analysis of a Variable Speed Heat Pump for Frequency Regulation Through Direct Load Control , 2015, IEEE Transactions on Power Systems.
[14] Young-Jin Kim,et al. Optimal Price Based Demand Response of HVAC Systems in Multizone Office Buildings Considering Thermal Preferences of Individual Occupants Buildings , 2018, IEEE Transactions on Industrial Informatics.
[15] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[16] Bing Liu,et al. U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .
[17] Young-Jin Kim,et al. Optimal Retail Pricing for Demand Response of HVAC Systems in Commercial Buildings Considering Distribution Network Voltages , 2019, IEEE Transactions on Smart Grid.
[18] Eleni Mangina,et al. Input variable selection for thermal load predictive models of commercial buildings , 2017 .
[19] Tianshu Wei,et al. Deep reinforcement learning for building HVAC control , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).
[20] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[21] Afan Galih Salman,et al. Weather forecasting using deep learning techniques , 2015, 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS).
[22] Dimitris A. Pados,et al. On overfitting, generalization, and randomly expanded training sets , 2000, IEEE Trans. Neural Networks Learn. Syst..
[23] Nashwan Dawood,et al. Visualising urban energy use: the use of LiDAR and remote sensing data in urban energy planning , 2017 .
[24] Kyung-Bin Song,et al. An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.
[25] Peter Stone,et al. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science , 2017, Nature Communications.
[26] Yacine Rezgui,et al. A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control , 2018 .
[27] Siliang Lu,et al. A DEEP REINFORCEMENT LEARNING APPROACH TO USINGWHOLE BUILDING ENERGYMODEL FOR HVAC OPTIMAL CONTROL , 2018 .
[28] Martin Fodslette Møller,et al. A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.
[29] Jose M. Arroyo,et al. Bilevel programming applied to power system vulnerability analysis under multiple contingencies , 2010 .
[30] Daryoush Habibi,et al. Optimal Real-Time Residential Thermal Energy Management for Peak-Load Shifting With Experimental Verification , 2019, IEEE Transactions on Smart Grid.
[31] Andrew Kusiak,et al. Modeling and optimization of HVAC systems using a dynamic neural network , 2012 .
[32] Jeffrey M. Hausdorff,et al. Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson’s Disease , 2018, Scientific Reports.
[33] David H. Blum,et al. A novel multi-market optimization problem for commercial heating, ventilation, and air-conditioning systems providing ancillary services using multi-zone inverse comprehensive room transfer functions , 2016 .
[34] Antonio Liotta,et al. On-Line Building Energy Optimization Using Deep Reinforcement Learning , 2017, IEEE Transactions on Smart Grid.
[35] Zoltán Nagy,et al. Using machine learning techniques for occupancy-prediction-based cooling control in office buildings , 2018 .
[36] Thomas Morstyn,et al. Bilateral Contract Networks for Peer-to-Peer Energy Trading , 2019, IEEE Transactions on Smart Grid.
[37] Xinghuo Yu,et al. Multiparty Energy Management for Grid-Connected Microgrids With Heat- and Electricity-Coupled Demand Response , 2018, IEEE Transactions on Industrial Informatics.
[38] Johan Driesen,et al. Deep Reinforcement Learning for Optimal Control of Space Heating , 2018, ArXiv.
[39] Kaamran Raahemifar,et al. Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system , 2017 .
[40] Chika O. Nwankpa,et al. Physically-Based Building Load Model for Electric Grid Operation and Planning , 2017, IEEE Transactions on Smart Grid.
[41] Young-Jin Kim,et al. Power Hardware-in-the-Loop Simulation Study on Frequency Regulation Through Direct Load Control of Thermal and Electrical Energy Storage Resources , 2018, IEEE Transactions on Smart Grid.
[42] Andrew Kusiak,et al. Performance optimization of HVAC systems with computational intelligence algorithms , 2014 .
[43] Robert J. Marks,et al. Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter , 1995, IEEE Trans. Neural Networks.
[44] Julien Eynard,et al. Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings , 2015, Appl. Soft Comput..
[45] C. Y. Chung,et al. Economic MPC of Aggregating Commercial Buildings for Providing Flexible Power Reserve , 2015, IEEE Transactions on Power Systems.
[46] António E. Ruano,et al. The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems , 2016 .
[47] Yuanyuan Shi,et al. Modeling and optimization of complex building energy systems with deep neural networks , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.