Small-Scale Building Load Forecast based on Hybrid Forecast Engine

[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.