A review on machine learning forecasting growth trends and their real-time applications in different energy systems
Tài liệu tham khảo
Abdel-Aal, 1997, Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis, Energy, 22, 1059, 10.1016/S0360-5442(97)00032-7
Acikgoz, 2017, A novel ANN-based approach to estimate heat transfer coefficients in radiant wall heating systems, Energy and Buildings, 144, 401, 10.1016/j.enbuild.2017.03.043
Ahmad, 2018, Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches, Energy and Buildings, 166, 460, 10.1016/j.enbuild.2018.01.066
Ahmad, 2018, Utility companies strategy for short-term energy demand forecasting using machine learning based models, Sustainable Cities and Society, 39, 401, 10.1016/j.scs.2018.03.002
Ahmad, 2019, Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems, Sustainable Cities and Society, 45, 460, 10.1016/j.scs.2018.12.013
Ahmad, 2018, A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review, Energy and Buildings, 10.1016/j.enbuild.2018.01.017
Ahmad, 2019, Short-term energy prediction for district-level load management using machine learning based approaches, Vol. 158
Ahmad, 2018, Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment, Energy, 158, 17, 10.1016/j.energy.2018.05.169
Ahmad, 2018, Water source heat pump energy demand prognosticate using disparate data-mining based approaches, Energy, 152, 788, 10.1016/j.energy.2018.03.169
Ahmad, 2019, Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management, International Journal of Refrigeration, 98, 399, 10.1016/j.ijrefrig.2018.10.017
Aich, 2018, A scaled conjugate gradient backpropagation algorithm for keyword extraction, Advances in Intelligent Systems and Computing, 672, 674, 10.1007/978-981-10-7512-4_67
Akay, 2007, Grey prediction with rolling mechanism for electricity demand forecasting of Turkey, Energy, 32, 1670, 10.1016/j.energy.2006.11.014
Akkaya, 2007, DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive, Energy Conversion and Management, 48, 210, 10.1016/j.enconman.2006.04.022
Albadi, 2008, A summary of demand response in electricity markets, Electric Power Systems Research, 78, 1989, 10.1016/j.epsr.2008.04.002
Alhamazani, 2015, An overview of the commercial cloud monitoring tools: Research dimensions, design issues, and state-of-the-art, Computing, 97, 357, 10.1007/s00607-014-0398-5
Allende, 2017, Ensemble methods for time series forecasting, Vol. 349, 217
Almonacid, 2013, Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network, Energy, 53, 165, 10.1016/j.energy.2013.02.024
AL-Rousan, 2018, Advances in solar photovoltaic tracking systems: A review, Renewable and Sustainable Energy Reviews, 82, 2548, 10.1016/j.rser.2017.09.077
Amber, 2018, Natural convection induced by the absorption of solar radiation: A review, Renewable and Sustainable Energy Reviews, 82, 3526, 10.1016/j.rser.2017.10.106
Amin-Naseri, 2008, Combined use of unsupervised and supervised learning for daily peak load forecasting, Energy Conversion and Management, 49, 1302, 10.1016/j.enconman.2008.01.016
Archer, 1995, On some Bayesian/Regularization methods for image restoration, IEEE Transactions on Image Processing, 4, 989, 10.1109/83.392339
Ardakani, 2014, Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types, Energy, 65, 452, 10.1016/j.energy.2013.12.031
Arslan, 2011, ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study, Applied Thermal Engineering, 31, 3922, 10.1016/j.applthermaleng.2011.07.041
Atsalakis, 2016, Commodities’ price trend forecasting by a neuro-fuzzy controller, Energy Systems, 7, 73, 10.1007/s12667-015-0154-8
Baba-Ahmadi, 2017, Numerical simulations of wake characteristics of a horizontal axis tidal stream turbine using actuator line model, Renewable Energy, 113, 669, 10.1016/j.renene.2017.06.035
Bamdad, 2015, Inverse analysis of a rectangular fin using the lattice Boltzmann method, Energy Conversion and Management, 97, 290, 10.1016/j.enconman.2015.02.075
Barghinia, 2008, A combination method for short term load forecasting used in Iran electricity market by NeuroFuzzy, Bayesian and finding similar days methods, 0
Barhmi, 2019, Forecasting of wind speed using multiple linear regression and artificial neural networks, Energy Systems, 10.1007/s12667-019-00338-y
Bilgili, 2012, Electric energy demands of Turkey in residential and industrial sectors, Renewable and Sustainable Energy Reviews, 16, 404, 10.1016/j.rser.2011.08.005
Biswas, 2016, Prediction of residential building energy consumption: A neural network approach, Energy, 117, 84, 10.1016/j.energy.2016.10.066
Blumberg, 2012, 1
Boukelia, 2016, ANN-based optimization of a parabolic trough solar thermal power plant, Applied Thermal Engineering, 107, 1210, 10.1016/j.applthermaleng.2016.07.084
Bowden, 2008, Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models, Energy Economics, 30, 3186, 10.1016/j.eneco.2008.06.003
Brouns, 2017, Heat source discrimination in buildings to reconstruct internal gains from temperature measurements, Energy and Buildings, 135, 253, 10.1016/j.enbuild.2016.11.041
Buntine, 1991, Bayesian Back-propagation, Vol. 5, 603
Burden, 2008, Bayesian regularization of neural networks, Methods in Molecular Biology, 458, 25
Caballero, 2006, 2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks, Bioorganic & Medicinal Chemistry, 14, 3330, 10.1016/j.bmc.2005.12.048
Chen, 2016, A high-order modified Levenberg-Marquardt method for systems of nonlinear equations with fourth-order convergence, Applied Mathematics and Computation, 285, 79, 10.1016/j.amc.2016.03.031
Chen, 2009, Inverse estimation for the unknown frost geometry on the external wall of a forced-convection pipe, Energy Conversion and Management, 50, 1457, 10.1016/j.enconman.2009.02.017
Cheng, 2003, A simplified conjugate-gradient method for shape identification based on thermal data, Numerical Heat Transfer Part B Fundamentals, 43, 489, 10.1080/713836242
Cheng, 2014, CFD (computational fluid dynamics)-based optimal design of a micro-reformer by integrating computational a fluid dynamics code using a simplified conjugate-gradient method, Energy, 70, 355, 10.1016/j.energy.2014.04.005
Chu, 2017, Net load forecasts for solar-integrated operational grid feeders, Solar Energy, 158, 236, 10.1016/j.solener.2017.09.052
Clifford, 2006, Patient-centred advice is effective in improving adherence to medicines, Pharmacy World & Science, 28, 165, 10.1007/s11096-006-9026-6
Cornejo-Bueno, 2018, Bayesian optimization of a hybrid system for robust ocean wave features prediction, Neurocomputing, 275, 818, 10.1016/j.neucom.2017.09.025
Cui, 2016, A modified Levenberg-Marquardt algorithm for simultaneous estimation of multi-parameters of boundary heat flux by solving transient nonlinear inverse heat conduction problems, International Journal of Heat and Mass Transfer, 97, 908, 10.1016/j.ijheatmasstransfer.2016.02.085
Cui, 2017, A new approach for determining damping factors in Levenberg-Marquardt algorithm for solving an inverse heat conduction problem, International Journal of Heat and Mass Transfer, 107, 747, 10.1016/j.ijheatmasstransfer.2016.11.101
Da Silva, 2017, Performance analysis of neural network training algorithms and support vector machine for power generation forecast of photovoltaic panel, IEEE Latin America Transactions, 15, 1091, 10.1109/TLA.2017.7932697
Deb, 2017, A review on time series forecasting techniques for building energy consumption, Renewable and Sustainable Energy Reviews, 74, 902, 10.1016/j.rser.2017.02.085
Debnath, 2018, Forecasting methods in energy planning models, Renewable and Sustainable Energy Reviews, 88, 297, 10.1016/j.rser.2018.02.002
Deo, 2017, Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland, Renewable and Sustainable Energy Reviews, 72, 828, 10.1016/j.rser.2017.01.114
Derakhshandeh, 2018, A novel fuzzy logic Levenberg-Marquardt method to solve the ill-conditioned power flow problem, International Journal of Electrical Power & Energy Systems, 99, 299, 10.1016/j.ijepes.2018.01.019
Diebolt, 2003, Improving extremal fit: A Bayesian regularization procedure, Reliability Engineering & System Safety, 82, 21, 10.1016/S0951-8320(03)00096-6
Dkhichi, 2014, Parameter identification of solar cell model using Levenberg-Marquardt algorithm combined with simulated annealing, Solar Energy, 110, 781, 10.1016/j.solener.2014.09.033
Dodge, 2004, The Oxford dictionary of statistical terms, Vol. 46
Du, 2010, Convergence analysis of nonmonotone Levenberg-Marquardt algorithms for complementarity problem, Applied Mathematics and Computation, 216, 1652, 10.1016/j.amc.2010.03.021
Effat, 2017, Bayesian and hierarchical bayesian based regularization for deconvolving the distribution of relaxation times from electrochemical impedance spectroscopy data, Electrochimica Acta, 247, 1117, 10.1016/j.electacta.2017.07.050
Ekinci, 2015, Investigation and modeling of the tractive performance of radial tires using off-road vehicles, Energy, 93, 1953, 10.1016/j.energy.2015.10.070
Esen, 2009, Modelling of a vertical ground coupled heat pump system by using artificial neural networks, Expert Systems With Applications, 36, 10229, 10.1016/j.eswa.2009.01.055
Esen, 2008, Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system, Energy and Buildings, 40, 1074, 10.1016/j.enbuild.2007.10.002
Fan, 2009, A note on the Levenberg-Marquardt parameter, Applied Mathematics and Computation, 207, 351, 10.1016/j.amc.2008.10.056
Fan, 2005, On the quadratic convergence of the levenberg-marquardt method without nonsingularity assumption, Computing (Vienna/New York), 74, 23
Fannou, 2014, Modeling of a direct expansion geothermal heat pump using artificial neural networks, Energy and Buildings, 81, 381, 10.1016/j.enbuild.2014.06.040
Feng, 2017, Bayesian regularized quantile structural equation models, Journal of Multivariate Analysis, 154, 234, 10.1016/j.jmva.2016.11.002
Fernández, 2006, Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor, Journal of Molecular Graphics & Modelling, 25, 410, 10.1016/j.jmgm.2006.02.005
Fernandez, 2015, Genetic algorithm optimization of bayesian-regularized artificial neural networks in drug design, Artificial Neural Network for Drug Design, Delivery and Disposition, 83
Fischer, 2008, A Levenberg-Marquardt algorithm for unconstrained multicriteria optimization, Operations Research Letters, 36, 643, 10.1016/j.orl.2008.02.006
Fletcher, 2005, Function minimization by conjugate gradients, The Computer Journal, 7, 149, 10.1093/comjnl/7.2.149
Forouzanfar, 2010, Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran, Applied Energy, 87, 268, 10.1016/j.apenergy.2009.07.008
García-Hinde, 2018, Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting, Engineering Applications of Artificial Intelligence, 69, 157, 10.1016/j.engappai.2017.12.003
Gençay, 2001, Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging, IEEE Transactions on Neural Networks, 12, 726, 10.1109/72.935086
Gerdes, 2013, Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning, Expert Systems With Applications, 40, 5021, 10.1016/j.eswa.2013.03.025
Ghalehkhondabi, 2017, An overview of energy demand forecasting methods published in 2005–2015, Energy Systems, 8, 411, 10.1007/s12667-016-0203-y
Ghasemi, 2018, Detection of illegal consumers using pattern classification approach combined with Levenberg-Marquardt method in smart grid, International Journal of Electrical Power & Energy Systems, 99, 363, 10.1016/j.ijepes.2018.01.036
Ghosh, 2016, Development of the location suitability index for wave energy production by ANN and MCDM techniques, Renewable and Sustainable Energy Reviews, 59, 1017, 10.1016/j.rser.2015.12.275
Ghysels, 2014, Hiding global synchronization latency in the preconditioned Conjugate Gradient algorithm, Parallel Computing, 40, 224, 10.1016/j.parco.2013.06.001
Goktepe, 2008, Shear strength estimation of plastic clays with statistical and neural approaches, Building and Environment, 43, 849, 10.1016/j.buildenv.2007.01.022
Gonzales Chavez, 1999, Forecasting of energy production and consumption in Asturias (northern Spain), Energy, 24, 183, 10.1016/S0360-5442(98)00099-1
Goodarzi, 2010, QSPR predictions of heat of fusion of organic compounds using Bayesian regularized artificial neural networks, Chemometrics and Intelligent Laboratory Systems, 104, 260, 10.1016/j.chemolab.2010.08.018
Guo, 2011, A new approach to energy consumption prediction of domestic heat pump water heater based on grey system theory, Energy and Buildings, 43, 1273, 10.1016/j.enbuild.2011.01.001
Guo, 2018, Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving, Applied Energy, 225, 732, 10.1016/j.apenergy.2018.05.075
Guo, 2019, An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems, Applied Thermal Engineering, 1223, 10.1016/j.applthermaleng.2018.12.132
Gürgen, 2018, Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network, Renewable Energy, 117, 538, 10.1016/j.renene.2017.10.101
Haddad, 2015, ANNs-based modeling and prediction of hourly flow rate of a photovoltaic water pumping system: Experimental validation, Renewable and Sustainable Energy Reviews, 43, 635, 10.1016/j.rser.2014.11.083
Hagan, 1994, Training feedforward networks with the marquardt algorithm, IEEE Transactions on Neural Networks, 5, 989, 10.1109/72.329697
Hatti, 2011, Power management strategy in the alternative energy photovoltaic/PEM Fuel Cell hybrid system, Renewable and Sustainable Energy Reviews, 15, 5104, 10.1016/j.rser.2011.07.046
Hedayatizadeh, 2016, A review on plum drying, Renewable and Sustainable Energy Reviews, 56, 362, 10.1016/j.rser.2015.11.087
Heertjes, 2014, Self-tuning in integral sliding mode control with a Levenberg-Marquardt algorithm, Mechatronics, 24, 385, 10.1016/j.mechatronics.2013.05.005
Hemmati-Sarapardeh, 2018, On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment, Renewable and Sustainable Energy Reviews, 81, 313, 10.1016/j.rser.2017.07.049
Hestenes, 2012, Methods of conjugate gradients for solving linear systems, Journal of Research of the National Bureau of Standards, 49, 409, 10.6028/jres.049.044
Hirschen, 2006, Bayesian regularization neural networks for optimizing fluid flow processes, Computer Methods in Applied Mechanics and Engineering, 195, 481, 10.1016/j.cma.2005.01.015
Ho, 1990, Short term load forecasting of taiwan power system using a knowledge-based expert system, IEEE Transactions on Power Systems, 5, 1214, 10.1109/59.99372
Hong, 2018, An optimized gene expression programming model for forecasting the national CO2emissions in 2030 using the metaheuristic algorithms, Applied Energy, 228, 808, 10.1016/j.apenergy.2018.06.106
Houimli, 2019, Short-term electric load forecasting in Tunisia using artificial neural networks, Energy Systems
Huan, 2018, Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework, Computers and Electronics in Agriculture, 150, 257, 10.1016/j.compag.2018.04.022
Huang, 2017, Determination of optimal inclination function for external reflector of basin type still for maximum distillate productivity, Energy, 141, 1728, 10.1016/j.energy.2017.11.070
Huang, 2019, A novel energy demand prediction strategy for residential buildings based on ensemble learning, Vol. 158, 3411
Huddleston, 2018, Machine learning, 231
Hyndman, 2008, Density forecasting for long-term peak electricity demand, IEEE Transactions on Power Systems, 25, 1142
Jaafar, 2017, A review of dendritic growth during solidification: Mathematical modeling and numerical simulations, Renewable and Sustainable Energy Reviews, 74, 1064, 10.1016/j.rser.2017.02.050
Jacobs, 1986, A generalization of the conjugate-gradient method to solve complex systems, IMA Journal of Numerical Analysis, 6, 447, 10.1093/imanum/6.4.447
Jain, 2014, Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy, Applied Energy, 123, 168, 10.1016/j.apenergy.2014.02.057
James, 2017, Simulating current-energy converters: SNL-EFDC model development, verification, and parameter estimation, Renewable Energy
Jang, 2015, Optimization of a slab heating pattern for minimum energy consumption in a walking-beam type reheating furnace, Applied Thermal Engineering, 85, 313, 10.1016/j.applthermaleng.2015.04.029
Jiang, 2008, Nonlinear time series forecasting of time-delay neural network embedded with Bayesian regularization, Applied Mathematics and Computation, 205, 123, 10.1016/j.amc.2008.05.064
Kameli, 2012, Solution of inverse heat conduction problem using the lattice Boltzmann method, International Communications in Heat and Mass Transfer, 39, 1410, 10.1016/j.icheatmasstransfer.2012.07.032
Kameli, 2014, A new inverse method based on Lattice Boltzmann method for 1D heat flux estimation, International Communications in Heat and Mass Transfer, 50, 1, 10.1016/j.icheatmasstransfer.2013.11.014
Kaminski, 2015, An on-line trained neural controller with a fuzzy learning rate of the Levenberg-Marquardt algorithm for speed control of an electrical drive with an elastic joint, Applied Soft Computing Journal, 32, 509, 10.1016/j.asoc.2015.04.013
Kelley, 2011, Iterative methods for optimization
Khwaja, 2017, Boosted neural networks for improved short-term electric load forecasting, Electric Power Systems Research, 143, 431, 10.1016/j.epsr.2016.10.067
Kind, 2016, Plasma-sprayed coatings: Identification of plastic properties using macro-indentation and an inverse Levenberg-Marquardt method, Mechanics of Materials, 98, 22, 10.1016/j.mechmat.2016.03.003
Kişi, 2005, Comparison of three back-propagation training algorithms for two case studies, Indian Journal of Engineering and Materials Sciences, 12, 434
Klæboe, 2015, Benchmarking time series based forecasting models for electricity balancing market prices, Energy Systems, 6, 43, 10.1007/s12667-013-0103-3
Krishnamoorthy, 2017, A computationally efficient P1 radiation model for modern combustion systems utilizing pre-conditioned conjugate gradient methods, Applied Thermal Engineering, 119, 197, 10.1016/j.applthermaleng.2017.03.055
Kumar, 2015, Comparison of regression and artificial neural network models for estimation of global solar radiations, Renewable and Sustainable Energy Reviews, 52, 1294, 10.1016/j.rser.2015.08.021
Kuo, 2001, Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images, Breast Cancer Research and Treatment, 66, 51, 10.1023/A:1010676701382
Lauret, 2008, Bayesian neural network approach to short time load forecasting, Energy Conversion and Management, 49, 1156, 10.1016/j.enconman.2007.09.009
Lee, 2012, Inverse heat transfer analysis of a functionally graded fin to estimate time-dependent base heat flux and temperature distributions, Energy Conversion and Management, 57, 1, 10.1016/j.enconman.2011.12.002
Levenberg, 1943, A method for the solution of certain non-linear problems in Least Squares, Quarterly of Applied Mathematics, 1, 536
Li, 2009, Wind speed prediction based on genetic neural network, 2448
Li, 2017, Forecasting the REITs and stock indices: Group method of data handling neural network approach, Pacific Rim Property Research Journal, 23, 123, 10.1080/14445921.2016.1225149
Li, 2017, Urban biomass and methods of estimating municipal biomass resources, Renewable and Sustainable Energy Reviews, 80, 1017, 10.1016/j.rser.2017.05.214
Ligeza, 1976, Artificial intelligence: A modern approach, Neurocomputing, 9, 215, 10.1016/0925-2312(95)90020-9
Lin, 2006, Bayesian regularization and nonnegative deconvolution for room impulse response estimation, IEEE Transactions on Signal Processing, 54, 839, 10.1109/TSP.2005.863030
Lin, 2004, Theory of grey systems: Capturing uncertainties of grey information, Kybernetes, 33, 196, 10.1108/03684920410514139
Lin, 2017, Random forests-based extreme learning machine ensemble for multi-regime time series prediction, Expert Systems With Applications, 83, 164, 10.1016/j.eswa.2017.04.013
Lipton, 2015
Liu, 2014, New hybrid conjugate gradient method for unconstrained optimization, Applied Mathematics and Computation, 245, 36, 10.1016/j.amc.2014.07.096
Liu, 2006, Grey information theory and practical applications, Vol. 191–244, 275
Liu, 2014, Spectral method and its application to the conjugate gradient method, Applied Mathematics and Computation, 240, 339, 10.1016/j.amc.2013.12.094
Liu, 2018, Admittance-based load signature construction for non-intrusive appliance load monitoring, Energy and Buildings, 171, 209, 10.1016/j.enbuild.2018.04.049
Liu, 2018, Inverting methods for thermal reservoir evaluation of enhanced geothermal system, Renewable and Sustainable Energy Reviews, 82, 471, 10.1016/j.rser.2017.09.065
Lv, 2018, A Levenberg–Marquardt method for solving semi-symmetric tensor equations, Journal of Computational and Applied Mathematics, 332, 13, 10.1016/j.cam.2017.10.005
Ma, 2016, Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests, Applied Energy, 183, 193, 10.1016/j.apenergy.2016.08.096
MacKay, 1994, Bayesian nonlinear modeling for the prediction competition, ASHRAE Transactions, 100, 1053
MacKay, 1992, A practical bayesian framework for backpropagation networks, Neural Computation, 4, 448, 10.1162/neco.1992.4.3.448
Magoulès, 2016, Data mining and machine learning in building energy analysis, Data Mining and Machine Learning in Building Energy Analysis
Mahersia, 2015, Using multiple steerable filters and Bayesian regularization for facial expression recognition, Engineering Applications of Artificial Intelligence, 38, 190, 10.1016/j.engappai.2014.11.002
Mahersia, 2016, Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis, Computer Methods and Programs in Biomedicine, 126, 46, 10.1016/j.cmpb.2015.10.017
Mahmud, 2016, A poisson process model for activity forecasting, 3339
Mani-Varnosfaderani, 2016, Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks, Analytica Chimica Acta, 940, 56, 10.1016/j.aca.2016.08.046
Marquardt, 1963, An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11, 431, 10.1137/0111030
Martins, 2002, Molecular and biochemical characterization of a highly stable bacterial laccase that occurs as a structural component of the Bacillus subtilis endospore coat, The Journal of Biological Chemistry, 277, 18849, 10.1074/jbc.M200827200
Masood, 2016, Analysis of weight initialization routines for scaled conjugate gradient training algorithm, 533
Mat Daut, 2017, Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review, Renewable and Sustainable Energy Reviews, 70, 1108, 10.1016/j.rser.2016.12.015
Mazorra Aguiar, 2015, Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks, Solar Energy, 122, 1309, 10.1016/j.solener.2015.10.041
Mba, 2016, Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region, Energy and Buildings, 121, 32, 10.1016/j.enbuild.2016.03.046
McNeil, 2010, Modeling diffusion of electrical appliances in the residential sector, Energy and Buildings, 42, 783, 10.1016/j.enbuild.2009.11.015
Mishra, 2016, Analysis of levenberg-marquardt and scaled conjugate gradient training algorithms for artificial neural network based LS and MMSE estimated channel equalizers
Moftakhari, 2016, A novel energy simulation approach for thermal design of buildings equipped with radiative panels using inverse methodology, Energy and Buildings, 113, 169, 10.1016/j.enbuild.2015.12.007
Mohamed Ismail, 2012, Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends, Applied Energy, 92, 769, 10.1016/j.apenergy.2011.08.027
Moradi, 2013, The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield, Renewable Energy, 50, 915, 10.1016/j.renene.2012.08.070
Morii, 2018, Activity of invasive slug Limax maximus in relation to climate conditions based on citizen’s observations and novel regularization based statistical approaches, The Science of the Total Environment, 637–638, 1061, 10.1016/j.scitotenv.2018.04.403
Mostafavi, 2013, A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand, Energy Conversion and Management, 74, 548, 10.1016/j.enconman.2013.06.031
M鴏ler, 1993, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 525, 10.1016/S0893-6080(05)80056-5
Nadjiasngar, 2013, Gauss-Newton filtering incorporating Levenberg-Marquardt methods for tracking, Digital Signal Processing: A Review Journal, 23, 1662, 10.1016/j.dsp.2012.12.005
Nahmias, 2015, Production and operations analysis, 52
Nassiopoulos, 2014, Calibration of building thermal models using an optimal control approach, Energy and Buildings, 76, 81, 10.1016/j.enbuild.2014.02.052
Nguyen, 2005
Nguyen-Truong, 2015, An implementation of the Levenberg-Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks, Chemical Physics Letters, 629, 40, 10.1016/j.cplett.2015.04.019
Nikolaou, 2012, On the application of clustering techniques for office buildings’ energy and thermal comfort classification, IEEE Transactions on Smart Grid, 3, 2196, 10.1109/TSG.2012.2215059
Ou, 2018, Dynamic modeling and validation of a liquid desiccant cooling and dehumidification system, Energy and Buildings, 163, 44, 10.1016/j.enbuild.2017.12.041
Ozoegwu, 2018, The solar energy assessment methods for Nigeria: The current status, the future directions and a neural time series method, Renewable and Sustainable Energy Reviews, 92, 146, 10.1016/j.rser.2018.04.050
Panapakidis, 2014, Pattern recognition algorithms for electricity load curve analysis of buildings, Energy and Buildings, 73, 137, 10.1016/j.enbuild.2014.01.002
Pandey, 2012, Artificial neural network for predation of cooling load reduction using green roof over building in sustainable city, Sustainable Cities and Society, 3, 37, 10.1016/j.scs.2012.01.003
Pao, 2009, Forecasting energy consumption in Taiwan using hybrid nonlinear models, Energy, 34, 1438, 10.1016/j.energy.2009.04.026
Park, 2016, Approximate Bayesian MLP regularization for regression in the presence of noise, Neural Networks, 83, 75, 10.1016/j.neunet.2016.07.010
Parmar, 2011, Artificial neural network based modelling of desiccant wheel, Energy and Buildings, 43, 3505, 10.1016/j.enbuild.2011.09.016
Pereira, 2015, Empirical Bayesian regularization of the inverse acoustic problem, Applied Acoustics, 97, 11, 10.1016/j.apacoust.2015.03.008
Pérez Muñoz, 2013, Environmental applications of camera images calibrated by means of the Levenberg-Marquardt method, Computers & Geosciences, 51, 74, 10.1016/j.cageo.2012.07.016
Peterson, 1985, Method of conjugate gradients for the numerical solution of large body electromagnetic scattering problems, Journal of Optical Social American, 2, 971, 10.1364/JOSAA.2.000971
Peterson, 1986, Convergence of the conjugate gradient method when applied to matrix equations representing electromagnetic scattering problems, IEEE Transactions on Antennas and Propagation, 34, 1447, 10.1109/TAP.1986.1143780
Petri, 2014, A modular optimisation model for reducing energy consumption in large scale building facilities, Renewable and Sustainable Energy Reviews, 38, 990, 10.1016/j.rser.2014.07.044
Pocock, 1997, The complex bi-conjugate gradient solver applied to large electromagnetic scattering problems, computational costs, and cost scalings, IEEE Transactions on Antennas and Propagation, 45, 140, 10.1109/8.554251
Powell, 1977, Restart procedures for the conjugate gradient method, Mathematical Programming, 12, 241, 10.1007/BF01593790
Rahman, 1988, An expert system based algorithm for short term load forecast, IEEE Transactions on Power Systems, 3, 392, 10.1109/59.192889
Rahman, 2015, Operation and control strategies of integrated distributed energy resources: A review, Renewable and Sustainable Energy Reviews, 51, 1412, 10.1016/j.rser.2015.07.055
Raillon, 2018, An efficient Bayesian experimental calibration of dynamic thermal models, Energy, 152, 818, 10.1016/j.energy.2018.03.168
Rakhshkhorshid, 2014, Bayesian regularization neural networks for prediction of austenite formation temperatures (Ac1and Ac3), Journal of Iron and Steel Research International, 21, 246, 10.1016/S1006-706X(14)60038-8
Ranzato, 2014
Rao, 2018, Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study, Renewable and Sustainable Energy Reviews, 91, 248, 10.1016/j.rser.2018.03.096
Rodrigo, 2013, Models for the electrical characterization of high concentration photovoltaic cells and modules: A review, Renewable and Sustainable Energy Reviews, 26, 752, 10.1016/j.rser.2013.06.019
Rodrigo, 2014, Review of methods for the calculation of cell temperature in high concentration photovoltaic modules for electrical characterization, Renewable and Sustainable Energy Reviews, 38, 478, 10.1016/j.rser.2014.06.008
Rodriguez-Quinonez, 2013, Surface recognition improvement in 3D medical laser scanner using Levenberg-Marquardt method, Signal Processing, 93, 378, 10.1016/j.sigpro.2012.07.001
Rojas, 1996, Neural networks - a systematic introduction, Berlim: Springer-Verlag, 7, 509
2018, Rule-based autoregressive moving average models for forecasting load on special days: A case study for France, European Journal of Operational Research, 266, 259, 10.1016/j.ejor.2017.08.056
Saab, 2001, Univariate modeling and forecasting of energy consumption: The case of electricity in Lebanon, Energy, 26, 1, 10.1016/S0360-5442(00)00049-9
Sanstad, 2014, Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning, Applied Energy, 128, 265, 10.1016/j.apenergy.2014.04.096
Santosh Kumar, 2017, Performance analysis of Levenberg-Marquardt and Steepest Descent algorithms based ANN to predict compressive strength of SIFCON using manufactured sand, Engineering Science and Technology an International Journal, 20, 1396, 10.1016/j.jestch.2017.07.005
Sehgal, 2015, Artificial intelligence methods for oil price forecasting: A review and evaluation, Energy Systems, 6, 479, 10.1007/s12667-015-0151-y
Shafaghat, 2016, Methods for adaptive behaviors satisfaction assessment with energy efficient building design, Renewable and Sustainable Energy Reviews, 57, 250, 10.1016/j.rser.2015.12.133
Shi, 2017, Chaos time-series prediction based on an improved recursive Levenberg–Marquardt algorithm, Chaos, Solitons, and Fractals, 100, 57, 10.1016/j.chaos.2017.04.032
Shkvarko, 2004, Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed Data-part I: Theory, IEEE Transactions on Geoscience and Remote Sensing, 42, 923, 10.1109/TGRS.2003.823281
Sides, 2014, The victory lab: The secret science of winning campaigns, Public Opinion Quarterly, 78, 363, 10.1093/poq/nft048
Sitharthan, 2017, An Levenberg–Marquardt trained feed-forward back-propagation based intelligent pitch angle controller for wind generation system, Renewable Energy Focus, 22–23, 24, 10.1016/j.ref.2017.10.003
Smith, 1990, The biconjugate gradient method for electromagnetic scattering, IEEE Transactions on Antennas and Propagation, 38, 938, 10.1109/8.55595
Soares, 2018, Ensemble of evolving data clouds and fuzzy models for weather time series prediction, Applied Soft Computing Journal, 64, 445, 10.1016/j.asoc.2017.12.032
Some simple forecasting methods - OTexts. (n.d.). Www.Otexts.Org. Retrieved from https://www.otexts.org/fpp/2/3.
Song, 2005, Short-term load forecasting for the holidays using fuzzy linear regression method, IEEE Transactions on Power Systems, 20, 96, 10.1109/TPWRS.2004.835632
Sözen, 2005, Forecasting based on neural network approach of solar potential in Turkey, Renewable Energy, 30, 1075, 10.1016/j.renene.2004.09.020
Stepchenko, 2016, Nonlinear, non-stationary and seasonal time series forecasting using different methods coupled with data preprocessing, Procedia Computer Science, 104, 578, 10.1016/j.procs.2017.01.175
Sun, 2017, A Bayesian regularized artificial neural network for adaptive optics forecasting, Optics Communications, 382, 519, 10.1016/j.optcom.2016.08.035
Syranidis, 2018, Control techniques and the modeling of electrical power flow across transmission networks, Renewable and Sustainable Energy Reviews, 82, 3452, 10.1016/j.rser.2017.10.110
Taghvaee, 2013, A current and future study on non-isolated DC-DC converters for photovoltaic applications, Renewable and Sustainable Energy Reviews, 17, 216, 10.1016/j.rser.2012.09.023
Tam, 2016, Estimation of source location and ground impedance using a hybrid multiple signal classification and Levenberg-Marquardt approach, Journal of Sound and Vibration, 374, 279, 10.1016/j.jsv.2016.03.011
Tascikaraoglu, 2018, Evaluation of spatio-temporal forecasting methods in various smart city applications, Renewable and Sustainable Energy Reviews, 82, 424, 10.1016/j.rser.2017.09.078
The World Bank, 2018
Thompson, 1991, A study of methods of choosing the smoothing parameter in image restoration by regularization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 326, 10.1109/34.88568
Tiao, 1981, Modeling multiple time series with applications, Journal of the American Statistical Association, 76, 802
Titterington, 1985, General structure of regularization procedures in image reconstruction, Astronomy and Astrophysics, 144, 381
Tran, 2017, Bayesian model averaging for estimating the spatial temperature distribution in a steam methane reforming furnace, Chemical Engineering Research and Design, 131, 465, 10.1016/j.cherd.2017.09.027
Transtrum, 2012
Tsanas, 2012, Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools, Energy and Buildings, 49, 560, 10.1016/j.enbuild.2012.03.003
Übeyli, 2008, Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients, Computers in Biology and Medicine, 38, 401, 10.1016/j.compbiomed.2008.01.002
van der Meer, 2018, Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes, Applied Energy, 213, 195, 10.1016/j.apenergy.2017.12.104
Voyant, 2017, Machine learning methods for solar radiation forecasting: A review, Renewable Energy, 10.1016/j.renene.2016.12.095
Wang, 2017, A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models, Renewable and Sustainable Energy Reviews, 75, 796, 10.1016/j.rser.2016.10.079
Wang, 2012, Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?, Energy Economics, 34, 2167, 10.1016/j.eneco.2012.03.010
Wang, 2006, Simplified building model for transient thermal performance estimation using GA-based parameter identification, International Journal of Thermal Sciences, 45, 419, 10.1016/j.ijthermalsci.2005.06.009
Wang, 1999, Gray predicting theory and application of energy consumption of building heat-moisture system, Building and Environment, 34, 417, 10.1016/S0360-1323(98)00037-7
Wang, 2005, An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network, Journal of Computer-aided Molecular Design, 19, 137, 10.1007/s10822-005-3321-5
Wang, 2013, A hybrid dehumidifier model for real-time performance monitoring, control and optimization in liquid desiccant dehumidification system, Applied Energy, 111, 449, 10.1016/j.apenergy.2013.05.026
Wang, 2014, Changes in global ocean wave heights as projected using multimodel CMIP5 simulations, Geophysical Research Letters, 41, 1026, 10.1002/2013GL058650
Wang, 2015, Transient signal analysis based on Levenberg-Marquardt method for fault feature extraction of rotating machines, Mechanical Systems and Signal Processing, 54, 16, 10.1016/j.ymssp.2014.09.010
Wang, 2016, An inexact derivative-free Levenberg-Marquardt method for linear inequality constrained nonlinear systems under local error bound conditions, Applied Mathematics and Computation, 282, 32, 10.1016/j.amc.2016.01.063
Wang, 2017, Derivative-free restrictively preconditioned conjugate gradient path method without line search technique for solving linear equality constrained optimization, Computers & Mathematics With Applications, 73, 277, 10.1016/j.camwa.2016.11.025
Watrous, 1988, Learning algorithms for connectionist networks: Applied gradient methods of nonlinear optimization, Technical Reports (CIS), 597
Wongwises, 2017, A novel ANN-based approach to estimate heat transfer coefficients in radiant wall heating systems, Energy and Buildings, 144, 401, 10.1016/j.enbuild.2017.03.043
Xia, 2018, State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network, Energy, 153, 694, 10.1016/j.energy.2018.04.085
Xu, 2015, Forecasting energy consumption using a new GM-ARMA model based on HP filter: The case of Guangdong Province of China, Economic Modelling, 45, 127, 10.1016/j.econmod.2014.11.011
Yadav, 2014, Solar radiation prediction using Artificial Neural Network techniques: A review, Renewable and Sustainable Energy Reviews
Yadav, 2014, Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models, Renewable and Sustainable Energy Reviews, 31, 509, 10.1016/j.rser.2013.12.008
Yamashita, 2001, On the rate of convergence of the levenberg-marquardt method, Computing (Suppl), 15, 239
Yang, 2013, A higher-order Levenberg-Marquardt method for nonlinear equations, Applied Mathematics and Computation, 219, 10682, 10.1016/j.amc.2013.04.033
Yıldırım, 2018, Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey, Renewable and Sustainable Energy Reviews, 82, 1528, 10.1016/j.rser.2017.06.030
Yildiz, 2017, A review and analysis of regression and machine learning models on commercial building electricity load forecasting, Renewable and Sustainable Energy Reviews, 10.1016/j.rser.2017.02.023
Yuan, 2016, Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model, Energy, 100, 384, 10.1016/j.energy.2016.02.001
Yuan, 2017, Form-finding of tensegrity structures based on the Levenberg–Marquardt method, Computers & Structures, 192, 171, 10.1016/j.compstruc.2017.07.005
Yuce, 2014, Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study, Energy and Buildings, 80, 45, 10.1016/j.enbuild.2014.04.052
Yuce, 2016, ANN-GA smart appliance scheduling for optimised energy management in the domestic sector, Energy and Buildings, 111, 311, 10.1016/j.enbuild.2015.11.017
Yue, 2011, Bayesian regularization BP neural network model for predicting oil-gas drilling cost, 2, 483
Yusaf, 2010, CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network, Applied Energy, 87, 1661, 10.1016/j.apenergy.2009.10.009
Yusri, 2018, A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel, Renewable and Sustainable Energy Reviews, 90, 665, 10.1016/j.rser.2018.03.095
Zahedi, 2013, Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province - Canada, Energy, 49, 323, 10.1016/j.energy.2012.10.019
Zahedi, 2013, Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province - Canada, Energy, 49, 323, 10.1016/j.energy.2012.10.019
Zhang, 2012, Numerical optimization, Advances in Industrial Control, 31, 10.1007/978-1-4471-2224-1_2
Zhang, 2017, Operation status prediction based on top gas system analysis for blast furnace, IEEE Transactions on Control Systems Technology, 25, 262, 10.1109/TCST.2016.2547957
Zhang, 2018, Wavelet transform and Kernel-based extreme learning machine for electricity price forecasting, Energy Systems, 9, 113, 10.1007/s12667-016-0227-3
ZHANG, 2016, Improvement of Levenberg-Marquardt algorithm during history fitting for reservoir simulation, Petroleum Exploration and Development, 43, 876, 10.1016/S1876-3804(16)30105-7
Zheng, 2015, Variation of the wave energy and significant wave height in the China Sea and adjacent waters, Renewable and Sustainable Energy Reviews, 43, 381, 10.1016/j.rser.2014.11.001
Zheng, 2016, Numerical forecasting experiment of the wave energy resource in the China Sea, Advances in Meteorology, 2016, 10.1155/2016/5692431
Zheng, 2017, An overview of medium- to long-term predictions of global wave energy resources, Renewable and Sustainable Energy Reviews, 79, 1492, 10.1016/j.rser.2017.05.109
Zhou, 2008, A grey-box model of next-day building thermal load prediction for energy-efficient control, International Journal of Energy Research, 32, 1418, 10.1002/er.1458
Zhou, 2017, Estimation of relative permeability curves using an improved Levenberg-Marquardt method with simultaneous perturbation Jacobian approximation, Journal of Hydrology, 544, 604, 10.1016/j.jhydrol.2016.12.006
Zhu, 2007, An affine scaling reduced preconditional conjugate gradient path method for linear constrained optimization, Applied Mathematics and Computation, 184, 181, 10.1016/j.amc.2006.05.151
Zhu, 2008, Affine scaling interior Levenberg-Marquardt method for bound-constrained semismooth equations under local error bound conditions, Journal of Computational and Applied Mathematics, 219, 198, 10.1016/j.cam.2007.07.039
Zubir, 2017, Pattern classifier of chemical compounds in different qualities of agarwood oil parameter using scale conjugate gradient algorithm in MLP, 18