Small-Scale Building Load Forecast based on Hybrid Forecast Engine
暂无分享,去创建一个
[1] Mohammad Ghiasi,et al. Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .
[2] Nima Amjady,et al. Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm , 2018, Comput. Intell..
[3] Noradin Ghadimi,et al. A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets , 2017, J. Intell. Fuzzy Syst..
[4] Hamidreza Zareipour,et al. A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems , 2017, IEEE Transactions on Power Systems.
[5] Aref Jalili,et al. Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market , 2016, Complex..
[6] OVEIS ABEDINIA,et al. A new metaheuristic algorithm based on shark smell optimization , 2016, Complex..
[7] Noradin Ghadimi,et al. Short-term management of hydro-power systems based on uncertainty model in electricity markets , 2015 .
[8] Jane Labadin,et al. Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).
[9] Alireza Noruzi,et al. A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods , 2015, Complex..
[10] Hamid Shaker,et al. Short-term electricity load forecasting of buildings in microgrids , 2015 .
[11] Matteo De Felice,et al. Seasonal climate forecasts for medium-term electricity demand forecasting , 2015 .
[12] Wei-Peng Chen,et al. Neural network model ensembles for building-level electricity load forecasts , 2014 .
[13] Pawel Stepien,et al. Sliding Window Empirical Mode Decomposition -its performance and quality , 2014 .
[14] Noradin Ghadimi,et al. Firefly Technique Based on Optimal Congestion Management in an Electricity Market , 2014 .
[15] Rajesh Kumar,et al. Energy analysis of a building using artificial neural network: A review , 2013 .
[16] Noradin Ghadimi,et al. PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives , 2013, IEEE Systems Journal.
[17] Guillermo Escrivá-Escrivá,et al. Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model , 2013 .
[18] Tian Zheng,et al. Interaction-based feature selection and classification for high-dimensional biological data , 2012, Bioinform..
[19] Parag Kulkarni,et al. A mesh-radio-based solution for smart metering networks , 2012, IEEE Communications Magazine.
[20] Miltiadis Alamaniotis,et al. Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting , 2012, IEEE Transactions on Power Systems.
[21] Xi Fang,et al. 3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .
[22] Xin Yao,et al. Short-Term Load Forecasting with Neural Network Ensembles: A Comparative Study [Application Notes] , 2011, IEEE Computational Intelligence Magazine.
[23] Mohammad Shahidehpour,et al. Security-constrained expansion planning of fast-response units for wind integration ☆ , 2011 .
[24] Seema Singh,et al. Application of computational intelligence in emerging power systems , 2010 .
[25] Anna Scaglione,et al. On the impact of SmartGrid metering infrastructure on load forecasting , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[26] Qing Song,et al. On the Weight Convergence of Elman Networks , 2010, IEEE Transactions on Neural Networks.
[27] Charles Elkan,et al. Quadratic Programming Feature Selection , 2010, J. Mach. Learn. Res..
[28] Mohammad Shahidehpour,et al. ecurity-constrained expansion planning of fast-response units for wind ntegration , 2010 .
[29] Ashwani Kumar,et al. Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .
[30] Jie Zhao,et al. A new Elman neural network and its dynamic properties , 2008, 2008 IEEE Conference on Cybernetics and Intelligent Systems.
[31] Dong-Xiao Niu,et al. Intelligent short-term load forecasting based on pattern-base , 2008, 2008 International Conference on Machine Learning and Cybernetics.
[32] Faa-Jeng Lin,et al. Modified Elman neural network controller with improved particle swarm optimisation for linear synchronous motor drive , 2008 .
[33] Sylvain Meignen,et al. A New Formulation for Empirical Mode Decomposition Based on Constrained Optimization , 2007, IEEE Signal Processing Letters.
[34] Tao Qin,et al. Feature selection for ranking , 2007, SIGIR.
[35] Edward R. Dougherty,et al. The coefficient of intrinsic dependence (feature selection using el CID) , 2005, Pattern Recognit..
[36] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Christopher D. Blakely. A Fast Empirical Mode Decomposition Technique for Nonstationary Nonlinear Time Series , 2005 .
[38] Marko Robnik-Sikonja,et al. Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.
[39] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[40] S. J. Kiartzis,et al. Short term load forecasting using fuzzy neural networks , 1995 .
[41] A. Keyhani,et al. On-Line Weather-Sensitive And Industrial Group Bus Load Forecasting For Microprocessor-Based Applications , 1983, IEEE Transactions on Power Apparatus and Systems.