A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

An Algorithm for Least-Squares Estimation of Nonlinear Parameters

Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Extreme learning machine: Theory and applications

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212

Introduction to the theory of neural computation

Hertz, J., Krogh, A., Palmer, R., & Jensen, R. V. (1994) · The advanced book program · doi:10.1119/1.17491 · Cited by 8,477

Training feedforward networks with the Marquardt algorithm

Hagan, M., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,778

Recent advances in convolutional neural networks

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576

Introduction to the theory of neural computation

Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,410

International Conference on Learning Representations (ICLR 2013)

松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759

The Strength of Weak Learnability

Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600

Bayesian Interpolation

Mackay, D. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,440

Mathematical Problems in Engineering

(2022) · Mathematical Problems in Engineering · doi:10.1155/2629 · Cited by 4,033

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239

Ensemble based systems in decision making

Robi Polikar, A. (2006) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2006.1688199 · Cited by 3,031

Advances in Neural Information Processing Systems 20

Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701

2013 IEEE Conference on Computer Vision and Pattern Recognition

(2013) · doi:10.1109/cvpr19808.2013 · Cited by 2,439

What is the best multi-stage architecture for object recognition?

Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153

Machine learning methods for solar radiation forecasting: A review

Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836

Selecting and interpreting measures of thematic classification accuracy

Stehman, S. V. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00083-7 · Cited by 1,745

Kernel density estimation via diffusion

Botev, Z. I., Grotowski, J. F., & Kroese, D. P. (2010) · The Annals of Statistics · doi:10.1214/10-aos799 · Cited by 1,689

Review of photovoltaic power forecasting

Antonanzas, J., Osorio, N., Escobar, R. A., Urraca-Valle, R., Martínez-de-Pisón, F. J., & Antoñanzas-Torres, F. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.069 · Cited by 1,493

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

(2013) · doi:10.1109/icassp16080.2013 · Cited by 1,493

Gauss-Newton approximation to Bayesian learning

Foresee, F. D., & Hagan, M. (2002) · doi:10.1109/icnn.1997.614194 · Cited by 1,409

Auto-association by multilayer perceptrons and singular value decomposition

Bourlard, H., & Kamp, Y. (1988) · Biological Cybernetics · doi:10.1007/bf00332918 · Cited by 1,391

2009 IEEE 12th International Conference on Computer Vision

(2009) · doi:10.1109/iccv15264.2009 · Cited by 1,374

Serial Order: A Parallel Distributed Processing Approach

Jordan, M. I. (1997) · Advances in psychology · doi:10.1016/s0166-4115(97)80111-2 · Cited by 1,308

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,252

Forecasting of photovoltaic power generation and model optimization: a review

Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.017 · Cited by 1,211

A review on the forecasting of wind speed and generated power

Ma, L., Luan, S., Chuanwen, J., Hongling, L., & Zhang, Y. (2008) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.02.002 · Cited by 1,159

The "Wake-Sleep" Algorithm for Unsupervised Neural Networks

Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995) · Science · doi:10.1126/science.7761831 · Cited by 1,070

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza, M. Q., & Khosravi, A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.065 · Cited by 1,061

IEEE Winter Conference on Applications of Computer Vision

(2014) · doi:10.1109/wacv31811.2014 · Cited by 1,009

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationPreprint

He, K., Zhang, X., Ren, S., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.01852 · Cited by 1,000

Solar photovoltaic generation forecasting methods: A review

Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.019 · Cited by 990

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

Qing, X., & Niu, Y. (2018) · Energy · doi:10.1016/j.energy.2018.01.177 · Cited by 989

A review of wind power and wind speed forecasting methods with different time horizons

Soman, S. S., Zareipour, H., Malik, O. P., & Mandal, P. (2010) · North American Power Symposium (NAPS) · doi:10.1109/naps.2010.5619586 · Cited by 922

Solar–wind hybrid renewable energy system: A review

Khare, V., Nema, S., & Baredar, P. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.223 · Cited by 843

Online short-term solar power forecasting

Bacher, P., Madsen, H., & Nielsen, H. A. (2009) · Solar Energy · doi:10.1016/j.solener.2009.05.016 · Cited by 841

Review of solar irradiance forecasting methods and a proposition for small-scale insular grids

Diagne, M., David, M., Lauret, P., Boland, J. W., & Schmutz, N. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.06.042 · Cited by 808

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Shi, J., Lee, W., Liu, Y., et al. (2012) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2012.2190816 · Cited by 790

Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

Khosravi, A., Nahavandi, S., Creighton, D., et al. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2096824 · Cited by 765

Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

Khosravi, A., Nahavandi, S., Creighton, D., & Atiya, A. F. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2162110 · Cited by 725

Estimating the mean and variance of the target probability distribution

Nix, D. A., & Weigend, A. S. (1994) · doi:10.1109/icnn.1994.374138 · Cited by 720

Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks

Gensler, A., Henze, J., Sick, B., & Raabe, N. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844673 · Cited by 693

Predicting solar radiation at high resolutions: A comparison of time series forecasts

Reikard, G. E. (2008) · Solar Energy · doi:10.1016/j.solener.2008.08.007 · Cited by 679

Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Wang, S., Zhang, N., Wu, L., & Wang, Y. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.03.103 · Cited by 674

Assessment of forecasting techniques for solar power production with no exogenous inputs

Pedro, H. T. C., & Coimbra, C. F. M. (2012) · Solar Energy · doi:10.1016/j.solener.2012.04.004 · Cited by 646

Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

Pham, B. T., Bui, D. T., Prakash, I., & Dholakia, M. B. (2016) · CATENA · doi:10.1016/j.catena.2016.09.007 · Cited by 628

Deep learning for estimating building energy consumption

Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2016.02.005 · Cited by 627

Online 24-h solar power forecasting based on weather type classification using artificial neural network

Chen, C., Duan, S., Cai, T., et al. (2011) · Solar Energy · doi:10.1016/j.solener.2011.08.027 · Cited by 615

Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed

Chow, C., Urquhart, B., Lave, M., Dominguez, A., Kleissl, J. P., Shields, J. E., & Washom, B. (2011) · Solar Energy · doi:10.1016/j.solener.2011.08.025 · Cited by 604

Solar and terrestrial radiation dependent on the amount and type of cloud

Kasten, F., & Czeplak, G. (1980) · Solar Energy · doi:10.1016/0038-092x(80)90391-6 · Cited by 572

On recent advances in PV output power forecast

Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544

Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

Ren, C., An, N., Wang, J., Li, L., Hu, B., & Shang, D. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.11.015 · Cited by 542

Technology Roadmap: Solar Photovoltaic Energy

International Energy Agency (2010) · IEA technology roadmaps · doi:10.1787/9789264088047-en · Cited by 527

A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

Yang, H., Huang, C., Huang, Y.-C., & Pai, Y.-S. (2014) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2014.2313600 · Cited by 475

Transfer learning for short-term wind speed prediction with deep neural networks

Hu, Q., Zhang, R., & Zhou, Y. (2015) · Renewable Energy · doi:10.1016/j.renene.2015.06.034 · Cited by 469

Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach

Sezer, Ö. B., & Özbayoğlu, A. M. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.024 · Cited by 448

Ensemble methods for wind and solar power forecasting—A state-of-the-art review

Ren, Y., Suganthan, P. N., & Srikanth, N. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.081 · Cited by 435

Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review

Shivashankar, S., Mekhilef, S., Mokhlis, H., et al. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.01.059 · Cited by 425

Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting

Wen, L., Zhou, K., Yang, S., & Lu, X. (2019) · Energy · doi:10.1016/j.energy.2019.01.075 · Cited by 419

Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks

Bashir, Z. A., & El-Hawary, M. E. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2008.2008606 · Cited by 414

Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

Wang, H., Yi, H., Peng, J., Wang, G., Liu, Y., Jiang, H. Q., & Liu, W. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.10.008 · Cited by 407

Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States

Mathiesen, P., & Kleissl, J. (2011) · Solar Energy · doi:10.1016/j.solener.2011.02.013 · Cited by 401

Solar energy in progress and future research trends

Şen, Z. (2004) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2004.02.004 · Cited by 393

Power load forecasting using support vector machine and ant colony optimization

Niu, D., Wang, Y., & Wu, D. D. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.08.019 · Cited by 392

Forecasting of preprocessed daily solar radiation time series using neural networks

Paoli, C., Voyant, C., Muselli, M., & Nivet, M. L. (2010) · Solar Energy · doi:10.1016/j.solener.2010.08.011 · Cited by 381

Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

Sfetsos, A., & Coonick, A. H. (2000) · Solar Energy · doi:10.1016/s0038-092x(99)00064-x · Cited by 378

Wind power prediction using deep neural network based meta regression and transfer learning

Qureshi, A. S., Khan, A., Zameer, A., & Usman, A. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.05.031 · Cited by 368

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Liu, J. P., Fang, W., Zhang, X., & Yang, C. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2014.2381224 · Cited by 357

Fast Training of Convolutional Networks through FFTs

Mathieu, M., Henaff, M., & LeCun, Y. (2014) · International Conference on Learning Representations · Cited by 347

Long Term Probabilistic Load Forecasting and Normalization With Hourly Information

Hong, T., Wilson, J., & Xie, J. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2274373 · Cited by 343

Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting

Wang, F., Zhang, Z., Liu, C., Yu, Y., Pang, S., Duić, N., Shafie‐khah, M., & Catalào, J. P. S. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.11.074 · Cited by 342

Prediction Intervals for Artificial Neural Networks

Hwang, J. T. G., & Ding, A. A. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474027 · Cited by 328

A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data

Srivastava, S., & Lessmann, S. (2018) · Solar Energy · doi:10.1016/j.solener.2018.01.005 · Cited by 328

A review on the selected applications of forecasting models in renewable power systems

Ahmed, A., & Khalid, M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.09.046 · Cited by 326

Electric load forecasting by support vector model

Hong, W. (2008) · Applied Mathematical Modelling · doi:10.1016/j.apm.2008.07.010 · Cited by 314

Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

Cadenas, E., Rivera, W., Campos–Amezcua, R., & Heard, C. (2016) · Energies · doi:10.3390/en9020109 · Cited by 310

Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm

Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.134 · Cited by 310

2015 IEEE International Conference on Systems, Man, and Cybernetics

(2015) · doi:10.1109/smc35812.2015 · Cited by 310

An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed

Zhao, J., Guo, Z., Su, Z., et al. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.145 · Cited by 304

Wind Effect on PV Module Temperature: Analysis of Different Techniques for an Accurate Estimation

Schwingshackl, C., Petitta, M., Wagner, J., et al. (2013) · Energy Procedia · doi:10.1016/j.egypro.2013.08.010 · Cited by 299

3D Object Recognition with Deep Belief Nets

Nair, V. B., & Hinton, G. E. (2009) · Cited by 299

Experimental Analysis of the Real-time Recurrent Learning Algorithm

Williams, R. J., & Zipser, D. (1989) · Connection Science · doi:10.1080/09540098908915631 · Cited by 298

Regional PV power prediction for improved grid integration

Lorenz, E., Scheidsteger, T., Hurka, J., Heinemann, D., & Kurz, C. (2010) · Progress in Photovoltaics Research and Applications · doi:10.1002/pip.1033 · Cited by 294

Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies

Guo, Z., Zhou, K., Zhang, C., Lu, X., Chen, W. L., & Yang, S. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.07.046 · Cited by 293

Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks

Liu, H., Tian, H.-Q., Liang, X., et al. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.014 · Cited by 292

2016 European Control Conference (ECC)

(2016) · doi:10.1109/ecc38430.2016 · Cited by 290

Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm

Hong, W. (2011) · Energy · doi:10.1016/j.energy.2011.07.015 · Cited by 283

Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

Hossain, M., Mekhilef, S., Danesh, M., Olatomiwa, L. J., & Shamshirband, S. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.08.081 · Cited by 281

Prediction of short-term PV power output and uncertainty analysis

Liu, L., Zhao, Y., Chang, D., Xie, J., Ma, Z., Sun, Q., Yin, H., & Wennersten, R. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.112 · Cited by 281

Solar Irradiance Forecasting Using Deep Neural Networks

Alzahrani, A., Shamsi, P., Dagli, C., & Ferdowsi, M. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.09.045 · Cited by 280

Parametric study of hybrid (wind + solar + diesel) power generating systems

Elhadidy, M. A., & Shaahid, S. M. (2000) · Renewable Energy · doi:10.1016/s0960-1481(00)00040-9 · Cited by 274

Photovoltaic power forecasting using statistical methods: impact of weather data

Giorgi, M. G. D., Congedo, P. M., & Malvoni, M. (2014) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2013.0135 · Cited by 270

Short-term reforecasting of power output from a 48 MWe solar PV plant

Chu, Y., Urquhart, B., Gohari, S. M. I., Pedro, H. T. C., Kleissl, J. P., & Coimbra, C. F. M. (2014) · Solar Energy · doi:10.1016/j.solener.2014.11.017 · Cited by 267

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Nespoli, A., Ogliari, E., Leva, S., et al. (2019) · Energies · doi:10.3390/en12091621 · Cited by 265

North American Power Symposium 2010

(2010) · doi:10.1109/naps17227.2010 · Cited by 263

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Wang, F., Zhen, Z., Wang, B., & Mi, Z. (2017) · Applied Sciences · doi:10.3390/app8010028 · Cited by 258

Optimal scheduling of thermal-wind-solar power system with storage

Salkuti, S. R. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.10.022 · Cited by 256

A suite of metrics for assessing the performance of solar power forecasting

Zhang, J., Florita, A. R., Hodge, B. S., Lu, S., Hamann, H. F., Banunarayanan, V., & Brockway, A. M. (2014) · Solar Energy · doi:10.1016/j.solener.2014.10.016 · Cited by 254

A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production

Zamo, M., Mestre, O., Arbogast, P., & Pannekoucke, O. (2014) · Solar Energy · doi:10.1016/j.solener.2013.12.006 · Cited by 251

Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting

Wang, F., Zhen, Z., Mi, Z., et al. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.10.002 · Cited by 250

Modeling Solar Radiation at the Earth’s Surface

Bădescu, V. (2008) · Springer eBooks · doi:10.1007/978-3-540-77455-6 · Cited by 249

Solar and photovoltaic forecasting through post‐processing of the Global Environmental Multiscale numerical weather prediction model

Pelland, S., Galanis, G. N., & Kallos, G. B. (2011) · Progress in Photovoltaics Research and Applications · doi:10.1002/pip.1180 · Cited by 248

Short–mid-term solar power prediction by using artificial neural networks

İzgi, E., Öztopal, A., Yerli, B., Kaymak, M. K., & Şahin, A. D. (2011) · Solar Energy · doi:10.1016/j.solener.2011.11.013 · Cited by 246

Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

David, M., Ramahatana, F., Trombe, P., & Lauret, P. (2016) · Solar Energy · doi:10.1016/j.solener.2016.03.064 · Cited by 246

Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Behera, M. K., Majumder, I., & Nayak, N. (2018) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2018.04.013 · Cited by 231

Wind Power Prediction by a New Forecast Engine Composed of Modified Hybrid Neural Network and Enhanced Particle Swarm Optimization

Amjady, N., Keynia, F., & Zareipour, H. (2011) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2011.2114680 · Cited by 227

Physical and hybrid methods comparison for the day ahead PV output power forecast

Ogliari, E., Dolara, A., Manzolini, G., et al. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.05.063 · Cited by 227

Solar Forecasting: Methods, Challenges, and Performance

Tuohy, A., Zack, J. W., Haupt, S. E., et al. (2015) · IEEE Power and Energy Magazine · doi:10.1109/mpe.2015.2461351 · Cited by 222

Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation

Yang, C., Thatte, A. A., & Xie, L. (2014) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2014.2359974 · Cited by 221

Univariate and multivariate methods for very short-term solar photovoltaic power forecasting

Rana, M., Koprinska, I., & Agelidis, V. G. (2016) · Energy Conversion and Management · doi:10.1016/j.enconman.2016.05.025 · Cited by 221

Deep photovoltaic nowcasting

Zhang, J., Verschae, R., Nobuhara, S., & Lalonde, J. (2018) · Solar Energy · doi:10.1016/j.solener.2018.10.024 · Cited by 219

A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

Almonacid, F. M., Pérez-Higueras, P., Férnández, E. F., et al. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.05.090 · Cited by 218

The value of day-ahead solar power forecasting improvement

Brancucci, C., Botor, B., Florita, A. R., et al. (2016) · Solar Energy · doi:10.1016/j.solener.2016.01.049 · Cited by 215

Scalable stacking and learning for building deep architectures

Deng, L., Yu, D., & Platt, J. C. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288333 · Cited by 210

What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation

Lawrence, S., Giles, C. L., & Tsoi, A. C. (1998) · University Libraries (University of Maryland) · Cited by 208

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Mellit, A., Pavan, A. M., & Lughi, V. (2014) · Solar Energy · doi:10.1016/j.solener.2014.03.018 · Cited by 188

Handbook of Neural Computation

(2017) · Elsevier eBooks · doi:10.1016/c2016-0-01217-2 · Cited by 186

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(2013) · doi:10.1109/naps31839.2013 · Cited by 177

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(2010) · doi:10.1109/powercon16271.2010 · Cited by 165

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(2019) · Prognostics and System Health Management Conference · doi:10.1109/phm-paris.2019.00002 · Cited by 162

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(2011) · doi:10.1109/isgt19296.2011 · Cited by 159

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(1997) · doi:10.1109/icnn.1997.611621 · Cited by 122

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(2011) · doi:10.1109/iceice17633.2011 · Cited by 122

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(2012) · Journal of Lightwave Technology · doi:10.1109/jlt.2012.2192389 · Cited by 105

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(2016) · doi:10.1109/icrera39003.2016 · Cited by 93

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(1994) · doi:10.1109/icnn.1994.374127 · Cited by 80

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