Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks
暂无分享,去创建一个
Sieh Kiong Tiong | Mijanur Rahman | Fatema Khatun | Jagadeesh Pasupuleti | Nowshad Amin | Mohammad Shakeri | Mohammad Kamrul Hasan | N. Amin | S. Tiong | J. Pasupuleti | Mijanur Rahman | Mohammad Shakeri | F. Khatun | M. K. Hasan
[1] Amir Mosavi,et al. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road) , 2019, Engineering Applications of Computational Fluid Mechanics.
[2] Amith Khandakar,et al. Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar , 2019, Energies.
[3] F. Manzano-Agugliaro,et al. Proposed methodology for evaluation of small hydropower sustainability in a Mediterranean climate , 2019, Journal of Cleaner Production.
[4] Mark Jaccard,et al. Combining Top-Down and Bottom-Up Approaches To Energy-Economy Modeling Using Discrete Choice Methods , 2005 .
[5] Stefan Lessmann,et al. A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data , 2018 .
[6] Haritza Camblong,et al. A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation , 2018 .
[7] Yang Zhenglin,et al. Improved Cluster Analysis Based Ultra-short Term Load Forecasting Method , 2005 .
[8] Wei Chen,et al. A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model , 2020, Mathematical Problems in Engineering.
[9] Hideo Kobayashi,et al. Application of artificial neural network to forecasting methods of time variation of the flow rate into a dam for a hydro-power plant , 1993, [1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems.
[10] Na Dong,et al. A novel convolutional neural network framework based solar irradiance prediction method , 2020 .
[11] Runar Skagestad. Electricity Demand Forecasting with Gaussian Process Regression , 2018 .
[12] Inés María Galván,et al. A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models , 2014, IDC.
[13] Zoltán Nagy,et al. Using machine learning techniques for occupancy-prediction-based cooling control in office buildings , 2018 .
[14] C. Yoo,et al. Hydrogen-based self-sustaining integrated renewable electricity network (HySIREN) using a supply-demand forecasting model and deep-learning algorithms , 2019, Energy Conversion and Management.
[15] Md. Mijanur Rahman,et al. Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition , 2013, ArXiv.
[16] Nora El-Gohary,et al. A review of data-driven building energy consumption prediction studies , 2018 .
[17] Abdul Rahim Pazikadin,et al. Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend. , 2020, The Science of the total environment.
[18] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[19] Zafer Cömert,et al. Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models , 2020 .
[20] Chul-Yong Lee,et al. Forecasting new and renewable energy supply through a bottom-up approach: The case of South Korea , 2017 .
[21] Alok Kumar Mishra,et al. Application of neural networks in wind power (generation) prediction , 2009, 2009 International Conference on Sustainable Power Generation and Supply.
[22] Sumanta Pasari,et al. Wind Energy Prediction Using Artificial Neural Networks , 2020, Sustainable Production, Life Cycle Engineering and Management.
[23] M. C. Deo,et al. Forecasting wind with neural networks , 2003 .
[24] Zbigniew Leonowicz,et al. Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm , 2020, Energies.
[25] M. Hejazi,et al. Effect of intermittent operation on performance of a solar-powered membrane distillation system , 2019, Separation and Purification Technology.
[26] Germain Forestier,et al. Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.
[27] Wei Lee Woon,et al. Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey , 2014, DARE.
[28] Eklas Hossain,et al. HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey , 2020, IEEE Access.
[29] Ronald M. Summers,et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.
[30] H. Jacobsen. Integrating the bottom-up and top-down approach to energy–economy modelling: the case of Denmark , 1998 .
[31] E. Macchi,et al. The potential role of solid biomass for rural electrification: A techno economic analysis for a hybrid microgrid in India , 2016 .
[32] Bao Chau Phan,et al. Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid , 2019, Applied Sciences.
[33] Musse Mohamud Ahmed,et al. Measurement and Modeling of DTCR Software Parameters Based on Intranet Wide Area Measurement System for Smart Grid Applications , 2021 .
[34] Sung-Hoon Ahn,et al. A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources , 2014 .
[35] R. P. Saini,et al. A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications , 2016 .
[36] Sumanta Pasari,et al. Time Series Auto-Regressive Integrated Moving Average Model for Renewable Energy Forecasting , 2020 .
[37] C. Draxl,et al. Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development , 2019, Bulletin of the American Meteorological Society.
[38] Ozgur Kisi,et al. Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam , 2008 .
[39] Mehdi Khashei,et al. A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting , 2017 .
[40] James Mubiru,et al. Using Artificial Neural Networks to Predict Direct Solar Irradiation , 2011, Adv. Artif. Neural Syst..
[41] Álvaro Alonso,et al. Regression tree ensembles for wind energy and solar radiation prediction , 2017, Neurocomputing.
[42] Ashwani Kumar,et al. Renewable energy in India: Current status and future potentials , 2010 .
[43] Djamila Rekioua. Hybrid Renewable Energy Systems , 2020, Green Energy and Technology.
[44] Geun Ho Gu,et al. Machine learning for renewable energy materials , 2019, Journal of Materials Chemistry A.
[45] Sameer Qazi,et al. Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios , 2020, IEEE Access.
[46] C. Yoo,et al. A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea , 2020 .
[47] Mohd Amran Mohd Radzi,et al. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review , 2012 .
[48] Sang Jib Kwon,et al. Renewable electricity generation systems for electric-powered taxis: The case of Daejeon metropolitan city , 2016 .
[49] Rhee,et al. A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets , 2019, Applied Sciences.
[50] John Pastor,et al. Biomass prediction using generalized allometric regressions for some northeast tree species , 1984 .
[51] Heetae Kim,et al. Optimal hybrid renewable airport power system Empirical study on Incheon international airport, South Korea , 2016 .
[52] A. Louche,et al. Forecasting and simulating wind speed in Corsica by using an autoregressive model , 2003 .
[53] P SenthilKumar. Improved Prediction of Wind Speed using Machine Learning , 2019 .
[54] Abinet Tesfaye Eseye,et al. Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information , 2018 .
[55] Carlos Sánchez,et al. Hybrid biomass-wind power plant for reliable energy generation , 2010 .
[56] Sean F. Kennedy. Indonesia’s energy transition and its contradictions: Emerging geographies of energy and finance , 2018, Energy Research & Social Science.
[57] Syed Farooq Ali,et al. Techno economic analysis of a wind-photovoltaic-biomass hybrid renewable energy system for rural electrification: A case study of Kallar Kahar , 2018 .
[58] C. Yoo,et al. Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks , 2019, Energy.
[59] Ravinesh C. Deo,et al. Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia , 2019, Journal of Cleaner Production.
[60] Hussein A. Kazem,et al. Comparison of prediction methods of photovoltaic power system production using a measured dataset , 2017 .
[61] Zheng-xin Wang,et al. Forecasting the residential solar energy consumption of the United States , 2019, Energy.
[62] J. R. Rodríguez Díaz,et al. Hydropower energy recovery in irrigation networks: Validation of a methodology for flow prediction and pump as turbine selection , 2020 .
[63] S. M. Shaahid,et al. Optimal sizing of battery storage for hybrid (wind+diesel) power systems , 1999 .
[64] Tansu Filik,et al. Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir , 2017 .
[65] Adolfo Crespo Márquez,et al. A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources , 2019, Applied Sciences.
[66] Bimal K. Bose,et al. Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems—Some Example Applications , 2017, Proceedings of the IEEE.
[67] Anthony Lopez,et al. Renewable Energy Data, Analysis, and Decisions: A Guide for Practitioners , 2018 .
[68] Kaile Su,et al. Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano's Continuous Note Recognition , 2017, J. Robotics.
[69] Neelamegam Premalatha,et al. Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms , 2016 .
[70] Muhammad Naveed Naz,et al. Intermittent Wind Energy Assisted Micro-Grid Stability Enhancement Using Security Index Currents , 2019, 2019 15th International Conference on Emerging Technologies (ICET).
[71] Rachid Tadili,et al. Generation of Horizontal Hourly Global Solar Radiation From Exogenous Variables Using an Artificial Neural Network in Fes (Morocco) , 2017, International Journal of Renewable Energy Research.
[72] Bernhard Sick,et al. Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[73] Farhan Ullah,et al. A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis , 2019, Energies.
[74] J. Béjar,et al. Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level , 2019, Journal of Physics: Conference Series.
[75] Jordan M. Malof,et al. Mapping solar array location, size, and capacity using deep learning and overhead imagery , 2019, ArXiv.
[76] Jabar H. Yousif,et al. A Comparison Study Based on Artificial Neural Network for Assessing PV/T Solar Energy Production , 2019, Case Studies in Thermal Engineering.
[77] Xu Zhang,et al. Machine learning: Accelerating materials development for energy storage and conversion , 2020, InfoMat.
[78] M. Wasielewski,et al. Advances in solar energy conversion. , 2019, Chemical Society reviews.
[79] Shahaboddin Shamshirband,et al. Estimating Daily Dew Point Temperature Using Machine Learning Algorithms , 2019, Water.
[80] Paras Mandal,et al. A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.
[81] Annamária R. Várkonyi-Kóczy,et al. Industrial applications of Big Data: State of the art survey , 2017 .
[82] Prodromos Chatziagorakis,et al. Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: the case of Olvio , 2016, Neural Computing and Applications.
[83] Jianchun Peng,et al. A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.
[84] Heetae Kim,et al. Optimal Hybrid Renewable Power System for an Emerging Island of South Korea: The Case of Yeongjong Island , 2015 .
[85] Danting Dong,et al. Wind Power Prediction Based on Recurrent Neural Network with Long Short-Term Memory Units , 2018, 2018 International Conference on Renewable Energy and Power Engineering (REPE).
[86] Nilay Shah,et al. Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression , 2019, Renewable and Sustainable Energy Reviews.
[87] Peng Guo,et al. A Review of Wind Power Forecasting Models , 2011 .
[88] Ahmed N. Abdalla,et al. Prediction of small hydropower plant power production in Himreen Lake dam (HLD) using artificial neural network , 2017 .
[89] S. M. Shaahid,et al. Promoting applications of hybrid ( wind+photovoltaic+diesel+battery ) power systems in hot regions , 2004 .
[90] Shengnan Lu,et al. Performance Analysis of Various Activation Functions in Artificial Neural Networks , 2019, Journal of Physics: Conference Series.
[91] Xueqing Zhang,et al. A comprehensive review on the application of artificial neural networks in building energy analysis , 2019, Neurocomputing.
[92] D. M. Vinod Kumar,et al. Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review , 2020, Engineering Reports.
[93] José A. S. Sá,et al. Artificial neural networks approaches for predicting the potential for hydropower generation: a case study for Amazon region , 2019, J. Intell. Fuzzy Syst..
[94] Eklas Hossain,et al. Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review , 2019, IEEE Access.
[95] William E. Roper,et al. Energy demand estimation of South Korea using artificial neural network , 2009 .
[96] Boualem Hadjerioua,et al. Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model , 2017 .
[97] S. Rehman,et al. Assessment of wind energy potential using wind energy conversion system , 2019, Journal of Cleaner Production.
[98] T. Stokelj,et al. Application of neural networks for hydro power plant water inflow forecasting , 2000, Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering. NEUREL 2000 (IEEE Cat. No.00EX287).
[99] Yitao Liu,et al. Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network , 2017 .
[100] Yanfei Li,et al. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM , 2018 .
[101] Halim Ceylan,et al. Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey , 2008 .
[102] Shahaboddin Shamshirband,et al. Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System , 2019, Energies.
[103] R. Deo,et al. Computational intelligence approach for modeling hydrogen production: a review , 2018 .
[104] P. A. Rocha,et al. Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region , 2019, International Journal of Energy and Environmental Engineering.
[105] Hui Liu,et al. Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods , 2019, Energy Conversion and Management.
[106] Sergio Rivera,et al. Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: mathematical expected value and verification by Monte Carlo simulation , 2019, International Journal of Power and Energy Conversion.
[107] Aisha Hassan Abdalla Hashim,et al. A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications , 2020, Wireless Personal Communications.
[108] Yongho Ko,et al. A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans , 2017 .
[109] P. Senthil Kumar. Improved Prediction of Wind Speed using Machine Learning , 2019, EAI Endorsed Trans. Energy Web.
[110] Damir J. Đozić,et al. Application of artificial neural networks for testing long-term energy policy targets , 2019, Energy.
[111] Fei Wang,et al. Forecast of Solar Energy Production - A Deep Learning Approach , 2018, 2018 IEEE International Conference on Big Knowledge (ICBK).
[112] Xiangning Lin,et al. Hybrid renewable microgrid optimization techniques: A review , 2018 .
[113] Prabodh Bajpai,et al. Hybrid renewable energy systems for power generation in stand-alone applications: A review , 2012 .
[114] Carlos Cardeira,et al. The Daily and Hourly Energy Consumption and Load Forecasting Using Artificial Neural Network Method: A Case Study Using a Set of 93 Households in Portugal☆ , 2014 .
[115] Fouzi Harrou,et al. Forecasting of Photovoltaic Solar Power Production Using LSTM Approach , 2020, Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems.
[117] Min-Gil Kim,et al. Optimal renewable power generation systems for Busan metropolitan city in South Korea , 2016 .
[118] Cécile Belleudy,et al. A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies , 2012, EURASIP J. Embed. Syst..
[119] S. Mekhilef,et al. Potential application of renewable energy for rural electrification in Malaysia , 2013 .
[120] Maria Puig-Arnavat,et al. Artificial Neural Networks for Thermochemical Conversion of Biomass , 2015 .
[121] Monjur Mourshed,et al. Forecasting methods in energy planning models , 2018 .
[122] Germán Ramos Ruiz,et al. Validation of calibrated energy models: Common errors , 2017 .
[123] Shengli Zhang,et al. Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach , 2019, Energies.
[124] Tek Tjing Lie,et al. Hourly global solar irradiation forecasting for New Zealand , 2015 .
[125] Fabrice Rossi,et al. Mean Absolute Percentage Error for regression models , 2016, Neurocomputing.
[126] Marian P. Kazmierkowski,et al. DSP-Based Control of Grid-Connected Power Converters Operating Under Grid Distortions , 2011, IEEE Transactions on Industrial Informatics.
[127] Daniel O’Leary,et al. Feature Selection and ANN Solar Power Prediction , 2017 .
[128] Shahaboddin Shamshirband,et al. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review , 2019, Energies.