Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg-Marquardt Algorithm-Based ANN
暂无分享,去创建一个
Zhenzhi Lin | Li Yang | Muhammad Waseem | Zhenzhi Lin | M. Waseem | Li Yang
[1] Peng Wang,et al. A Data-Driven Bottom-Up Approach for Spatial and Temporal Electric Load Forecasting , 2019, IEEE Transactions on Power Systems.
[2] Sang-Seung Lee,et al. Power system restoration plan using the characteristics of scale‐free networks , 2008 .
[3] Xiaofeng Guo,et al. Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.
[4] Yi Ding,et al. A Framework for Incorporating Demand Response of Smart Buildings Into the Integrated Heat and Electricity Energy System , 2019, IEEE Transactions on Industrial Electronics.
[5] Fredrik Wallin,et al. Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .
[6] Tao Hong. Big Data Analytics: Making the Smart Grid Smarter [Guest Editorial] , 2018 .
[7] Yi-Chun Du,et al. Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor , 2018, Sensors.
[8] C.S. Ozveren,et al. Short term load forecasting using Multiple Linear Regression , 2007, 2007 42nd International Universities Power Engineering Conference.
[9] Dejan Mumovic,et al. A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .
[10] Carlos E. Pedreira,et al. Neural networks for short-term load forecasting: a review and evaluation , 2001 .
[11] H. Pedro,et al. Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .
[12] Jung P. Shim,et al. Big Data and Analytics: Issues, Solutions, and ROI , 2015, Commun. Assoc. Inf. Syst..
[13] Tao Hong,et al. Load forecasting using 24 solar terms , 2018 .
[14] Marios M. Polycarpou,et al. Short Term Electric Load Forecasting: A Tutorial , 2007, Trends in Neural Computation.
[15] Mattheos Santamouris,et al. Cooling the buildings – past, present and future , 2016 .
[16] Lucas W. Davis,et al. Contribution of air conditioning adoption to future energy use under global warming , 2015, Proceedings of the National Academy of Sciences.
[17] P. Holtberg,et al. International Energy Outlook 2016 With Projections to 2040 , 2016 .
[18] E. Cian,et al. Global Energy Consumption in a Warming Climate , 2019 .
[19] K. Pavlou,et al. On the efficiency of night ventilation techniques applied to residential buildings , 2010 .
[20] Kenneth A. Loparo,et al. Big data analytics in power distribution systems , 2015, 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).
[21] Iain MacGill,et al. Appliance level data analysis of summer demand reduction potential from residential air conditioner control , 2019, Applied Energy.
[22] Md. Jahangir Hossain,et al. Peak electricity load forecasting using online support vector regression , 2016, 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).
[23] Natalija Koseleva,et al. Big Data in Building Energy Efficiency: Understanding of Big Data and Main Challenges , 2017 .
[24] Pu Wang,et al. Electric load forecasting with recency effect: A big data approach , 2016 .
[25] S. Riffat,et al. Introduction: Overview of Buildings and Passive Cooling Technique , 2019, Nocturnal Cooling Technology for Building Applications.
[26] Vladimiro Miranda,et al. Probabilistic solar power forecasting in smart grids using distributed information , 2015 .
[27] L.C.P. da Silva,et al. Smart demand for improving short-term voltage control on distribution networks , 2009 .
[28] Hao Yu,et al. Levenberg—Marquardt Training , 2011 .
[29] Robert C. Qiu,et al. Research on big data applications in Global Energy Interconnection , 2018 .
[30] Anzar Mahmood,et al. Day ahead load forecasting for IESCO using Artificial Neural Network and Bagged Regression Tree , 2018, 2018 1st International Conference on Power, Energy and Smart Grid (ICPESG).
[31] Dongpu Cao,et al. Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System , 2018, IEEE Transactions on Industrial Informatics.
[32] Arunesh Kumar Singh,et al. Load forecasting techniques and methodologies: A review , 2012, 2012 2nd International Conference on Power, Control and Embedded Systems.
[33] Francisco Martínez-Álvarez,et al. Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities , 2018 .
[34] S. Wilcox,et al. Users Manual for TMY3 Data Sets (Revised) , 2008 .
[35] V. Ismet Ugursal,et al. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .
[36] Abbas Khosravi,et al. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .
[37] Lars Nordström,et al. Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling , 2017, 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm).
[38] Ong Hang See,et al. A review of residential demand response of smart grid , 2016 .
[39] Tao Hong,et al. Short Term Electric Load Forecasting , 2012 .
[40] Fu Xiao,et al. Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm , 2018, Applied Energy.
[41] Miriam A. M. Capretz,et al. Energy Forecasting for Event Venues: Big Data and Prediction Accuracy , 2016 .
[42] Mohamed Abdel-Nasser,et al. Accurate photovoltaic power forecasting models using deep LSTM-RNN , 2017, Neural Computing and Applications.
[43] M. Sivak. Potential energy demand for cooling in the 50 largest metropolitan areas of the world: Implications for developing countries , 2009 .
[44] Ahmed Elragal,et al. Big Data Analytics: A Literature Review Paper , 2014, ICDM.
[45] Hamed Mohsenian-Rad,et al. Power systems big data analytics: An assessment of paradigm shift barriers and prospects , 2018, Energy Reports.
[46] Sean D. Campbell,et al. Weather Forecasting for Weather Derivatives , 2002 .
[47] Taskin Koçak,et al. A Survey on Smart Grid Potential Applications and Communication Requirements , 2013, IEEE Transactions on Industrial Informatics.
[48] Yonghong Kuang,et al. Smart home energy management systems: Concept, configurations, and scheduling strategies , 2016 .
[49] Jiawei Hao,et al. Short-term load forecasting with clustering–regression model in distributed cluster , 2017, Cluster Computing.
[50] Ahmed M. Shahat Osman. A novel big data analytics framework for smart cities , 2019, Future Gener. Comput. Syst..
[51] Hui Xiao,et al. Predicting the Energy Consumption of Residential Buildings for Regional Electricity Supply-Side and Demand-Side Management , 2019, IEEE Access.
[52] S. Borlase. Smart Grids : Infrastructure, Technology, and Solutions , 2016 .
[53] Davide Brunelli,et al. Electricity demand forecasting of single residential units , 2013, 2013 IEEE Workshop on Environmental Energy and Structural Monitoring Systems.
[54] Peter Amaize,et al. Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods , 2016 .
[55] P. Siano,et al. Assessing the benefits of residential demand response in a real time distribution energy market , 2016 .
[56] Yan Su,et al. An ARMAX model for forecasting the power output of a grid connected photovoltaic system , 2014 .
[57] Yunhao Liu,et al. Big Data: A Survey , 2014, Mob. Networks Appl..