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Applied energy, v.251, 2019년, pp.113353 -
Heydari, Azim (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University) , Astiaso Garcia, Davide (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University) , Keynia, Farshid (Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology) , Bisegna, Fabio (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University) , De Santoli, Livio (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University)
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Abstract Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel pred...
IRENA. 2018 renewable capacity statistics. IRENA; 2018.
Sol Energy Antonanzas 136 78 2016 10.1016/j.solener.2016.06.069 Review of photovoltaic power forecasting
Math Probl Eng Wang 2015 An experimental investigation of FNN model for wind speed forecasting using EEMD and CS
IEEE Trans Power Syst Papalexopoulos 5 1535 1990 10.1109/59.99410 A regression-based approach to short-term system load forecasting
Energy Econ Sigauke 33 882 2011 10.1016/j.eneco.2011.02.013 Prediction of daily peak electricity demand in South Africa using volatility forecasting models
IEEE Trans Power Syst Bakirtzis 10 1518 1995 10.1109/59.466494 Short term load forecasting using fuzzy neural networks
Electr Power Syst Res Khwaja 125 109 2015 10.1016/j.epsr.2015.03.027 Improved short-term load forecasting using bagged neural networks
Electr Power Syst Res Ghayekhloo 119 138 2015 10.1016/j.epsr.2014.09.002 A hybrid short-term load forecasting with a new data preprocessing framework
IEEE Trans Power Syst Chen 19 1821 2004 10.1109/TPWRS.2004.835679 Load forecasting using support vector machines: a study on EUNITE competition 2001
Electr Power Syst Res Li 122 96 2015 10.1016/j.epsr.2015.01.002 Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
Energy Hong 36 5568 2011 10.1016/j.energy.2011.07.015 Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm
Expert Syst Appl Niu 37 2531 2010 10.1016/j.eswa.2009.08.019 Power load forecasting using support vector machine and ant colony optimization
Energy Convers Manag Pai 46 2669 2005 10.1016/j.enconman.2005.02.004 Support vector machines with simulated annealing algorithms in electricity load forecasting
Energy Proc Heydari 159 154 2019 10.1016/j.egypro.2018.12.044 Renewable energies generation and carbon dioxide emission forecasting in nicrogrids and national grids using GRNN-GWO methodology
Bishop 1995 Neural networks for pattern recognition
J Am Stat Assoc Hwang 92 748 1997 10.1080/01621459.1997.10474027 Prediction intervals for artificial neural networks
Adv Neural Inform Process Syst Heskes 176 1997 Practical confidence and prediction intervals
IEEE Trans Neural Netw Khosravi 22 1341 2011 10.1109/TNN.2011.2162110 Comprehensive review of neural network-based prediction intervals and new advances
Electr Power Syst Res Khosravi 96 185 2013 10.1016/j.epsr.2012.11.007 A neural network-GARCH-based method for construction of Prediction Intervals
IEEE Trans Neural Netw Khosravi 22 337 2011 10.1109/TNN.2010.2096824 Lower upper bound estimation method for construction of neural network-based prediction intervals
Neurocomputing Quan 127 172 2014 10.1016/j.neucom.2013.08.020 Particle swarm optimization for construction of neural network-based prediction intervals
IEEE Trans Neural Networks Learn Syst Quan 25 303 2014 10.1109/TNNLS.2013.2276053 Short-term load and wind power forecasting using neural network-based prediction intervals
10.1109/IJCNN.2013.6706839 Rana M, Koprinska I, Khosravi A, Agelidis VG. Prediction intervals for electricity load forecasting using neural networks. The international joint conference on neural networks (IJCNN); 2013. p. 1-8. doi: 10.1109/IJCNN.2013.6706839.
Appl Energy Maatallah Othman 145 191 2015 10.1016/j.apenergy.2015.02.032 Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
Sol Energy David 133 55 2016 10.1016/j.solener.2016.03.064 Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models
Sol Energy Li 159 97 2018 10.1016/j.solener.2017.10.051 Interval prediction of solar power using an Improved Bootstrap method
Renew Energy Cheng 107 340 2017 10.1016/j.renene.2017.02.014 Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation
Renew Energy Naik 129 357 2018 10.1016/j.renene.2018.05.031 Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression
Appl Energy Liu 228 1783 2018 10.1016/j.apenergy.2018.07.050 A novel ensemble model of different mother wavelets for wind speed multi-step forecasting
Renew Energy Naik 136 701 2019 10.1016/j.renene.2019.01.006 A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression
Appl Energy Song 215 643 2018 10.1016/j.apenergy.2018.02.070 A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting
Appl Energy Li 228 2207 2018 10.1016/j.apenergy.2018.07.032 A wind speed interval prediction system based on multi-objective optimization for machine learning method
Renew Energy Akarslan 123 58 2018 10.1016/j.renene.2018.02.048 Novel short term solar irradiance forecasting models
Appl Math Model Jiang 67 101 2019 10.1016/j.apm.2018.10.019 Multi-objective algorithm for the design of prediction intervals for wind power forecasting model
Renew Energy Bouzgou 133 1055 2019 10.1016/j.renene.2018.10.096 Fast short-term global solar irradiance forecasting with wrapper mutual information
Appl Energy Jiang 235 786 2019 10.1016/j.apenergy.2018.11.012 A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting
Pan WT. A new evolutionary computation approach: Fruit Fly Optimization Algorithm. In: Conference of digital technology and innovation management 2011:382-91.
Knowledge-Based Syst Miti 89 446 2015 10.1016/j.knosys.2015.08.010 Chaotic fruit fly optimization algorithm
Adv Mater Res Dongxiao 863 2513 2014 Forecasting residential electricity based on FOAGMNN
Knowledge-Based Syst Pan 26 69 2012 10.1016/j.knosys.2011.07.001 A new Fruit Fly Optimization Algorithm: taking the financial distress model as an example
Neural Networks Shrestha 19 225 2006 10.1016/j.neunet.2006.01.012 Machine learning approaches for estimation of prediction interval for the model output
IEEE Trans Neural Netw Papadopoulos 12 1278 2001 10.1109/72.963764 Confidence estimation methods for neural networks: a practical comparison
IEEE Trans Electron Packag Manuf Ho 2001 24 323 2001 10.1109/6104.980042 Neural network modeling with confidence bounds: a case study on the solder paste deposition process
J Am Stat Assoc Winkler 67 37 1972 10.1080/01621459.1972.10481224 A decision-theoretic approach to interval estimation
Int Conf Neural Networks (IJCNN) IEEE Quan 1 2012 Construction of neural network-based prediction intervals using particle swarm optimization
Appl Energy Yang 230 1108 2018 10.1016/j.apenergy.2018.09.037 A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm
Adv Adapt Data Anal Yeh 2 135 2010 10.1142/S1793536910000422 Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method
Adv Adapt Data Anal Wu 1 1 2009 10.1142/S1793536909000047 Ensemble empirical mode decomposition: a noise-assisted data analysis method
Eng Struct Ovanesova 26 39 2004 10.1016/j.engstruct.2003.08.009 Applications of wavelet transforms to damage detection in frame structures
NDT E Int Ni 2017 87 50 2017 10.1016/j.ndteint.2017.01.007 Evaluation of pile defects using complex continuous wavelet transform analysis
IEEE Trans Power Syst Reis 20 189 2005 10.1109/TPWRS.2004.840380 Feature extraction via multiresolution analysis for short-term load forecasting
IEEE Trans Pattern Anal Mach Intell Mallat 11 674 1989 10.1109/34.192463 A theory for multiresolution signal decomposition: the wavelet representation
IEEE Trans Ind Electron Iyer 60 1638 2013 10.1109/TIE.2012.2188258 A twofold daubechies-wavelet-based module for fault detection and voltage regulation in SEIGs for distributed wind power generation
Eng Appl Artif Intell Keynia 25 1687 2012 10.1016/j.engappai.2011.12.001 A new feature selection algorithm and composite neural network for electricity price forecasting
Oviet Autom Control Ivakhnenko 13 43 1968 The group method of data of handling; A revival of the method of stochastic approximation
Farlow 54 1984 Self-organizing methods in modeling: GMDH type algorithms
Appl Math Comput Shaghaghi 313 271 2017 Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design
Sustain Energy Technol Assessments Nezhad 30 68 2018 10.1016/j.seta.2018.08.009 Nearshore wave energy converters comparison and Mediterranean small island grid integration
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