Visualizing solar irradiance data in ArcGIS and forecasting based on a novel deep neural network mechanism

Banalaxmi Brahma1, Rajesh Wadhvani1
1Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

Aburto L, Weber R (2007) Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing Journal 7(1):136–144. https://doi.org/10.1016/j.asoc.2005.06.001

Alex G, Greg W, Ivo D (2014) Neural turing machines

Amrouche B, Le Pivert X (2014) Artificial neural network based daily local forecasting for global solar radiation. Appl Energy 130:333–341. https://doi.org/10.1016/j.apenergy.2014.05.055

Amrouche B, Sicot L, Guessoum A, Belhamel M (2013) Experimental analysis of the maximum power point’s properties for four photovoltaic modules from different technologies: Monocrystalline and polycrystalline silicon, CIS and CdTe, vol 118

Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473

Berardi VL, Zhang GP (2003) An empirical investigation of bias and variance in time series forecasting: Modeling considerations and error evaluation. IEEE Transactions on Neural Networks 14(3):668–679. https://doi.org/10.1109/TNN.2003.810601

Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015) Time Series Analysis: Forecasting & Control

Brahma B, Wadhvani R (2020) Solar irradiance forecasting based on deep learning methodologies and multi-site data. Symmetry, 12(11). https://doi.org/10.3390/sym12111830, https://www.mdpi.com/2073-8994/12/11/1830

Brahma B, Wadhvani R (2020) Time series forecasting: A comparison of deepneural network techniques. Solid State Technology 63:1747–1761. http://solidstatetechnology.us/index.php/JSST/article/view/2404

Brahma B, Wadhvani R, Shukla S (0) Attention mechanism for developing wind speed and solar irradiance forecasting models. Wind Eng 0(0):0309524X20981885. https://doi.org/10.1177/0309524X20981885

Brockwell PJ, Davis RA (2002) Introduction to Time Series and Forecasting - Second Edition

Creal D, Koopman SJ, Lucas A (2013) Generalized autoregressive score models with applications. J Appl Econom 28(5):777–795. https://doi.org/10.1002/jae.1279

Dong N, Chang J-F, Wu A-G, Gao Z-K (2020) A novel convolutional neural network framework based solar irradiance prediction method. International Journal of Electrical Power and Energy Systems 114:105411. https://doi.org/10.1016/j.ijepes.2019.105411. http://www.sciencedirect.com/science/article/pii/S0142061518332915

Dozat T (2016) Incorporating nesterov momentum into adam

Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(61):2121–2159. http://jmlr.org/papers/v12/duchi11a.html

Geron A (2017) Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems

Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. In: Neural Networks, 18, pp 602–610

Greff K, Srivastava RK, Koutnik J, Steunebrink B, Schmidhuber J (2017) LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems 28(10):2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924

Guermoui M, Melgani F, Gairaa K, Mekhalfi ML (2020) A comprehensive review of hybrid models for solar radiation forecasting. J Clean Prod 258:120357. https://doi.org/10.1016/j.jclepro.2020.120357. http://www.sciencedirect.com/science/article/pii/S0959652620304042

Heng J, Wang J, Xiao L, Lu H (2017) Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting. Appl Energy 208:845–866. https://doi.org/10.1016/j.apenergy.2017.09.063

Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

hsiang Wang C, Grozev G, Seo S (2012) Decomposition and statistical analysis for regional electricity demand forecasting. Energy 41(1):313–325. https://doi.org/10.1016/j.energy.2012.03.011

Ilya S, Oriol V, Quoc VL (2014) Sequence to sequence learning with neural networks

Kariniotakis G (2017) Renewable energy forecasting: From models to applications

Kim TY, Cho SB (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81. https://doi.org/10.1016/j.energy.2019.05.230

Kingma DP, Ba JL (2015) Adam: A method for stochastic gradient descent. ICLR: International Conference on Learning Representations

Kyunghyun C, Bart VM, Caglar G, Dzmitry B, Fethi B, Holger S, Yoshua B (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation

LeCun Y, Haffner P, Bottou L, Bengio Y Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Li T, Hua M, Wu X (2020) A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5). IEEE Access 8:26933–26940. https://doi.org/10.1109/ACCESS.2020.2971348

Li Y, Su Y, Shu L (2014) An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renew Energy 66:78–89. https://doi.org/10.1016/j.renene.2013.11.067

Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with restarts. CoRR abs/1608.03983, 1608.03983

Lubitz WD (2011) Effect of manual tilt adjustments on incident irradiance on fixed and tracking solar panels. Appl Energy 88 (5):1710–1719. https://doi.org/10.1016/j.apenergy.2010.11.008

Mateo F, Carrasco JJe, Sellami A, Millán-Giraldo M, Domínguez M, Soria-Olivas E (2013) Machine learning methods to forecast temperature in buildings. Expert Syst Appl 40(4):1061–1068. https://doi.org/10.1016/j.eswa.2012.08.030

Neves C, Fernandes C, Hoeltgebaum H (2017) Five different distributions for the Lee-Carter model of mortality forecasting: A comparison using GAS models. Insurance: Mathematics and Economics 75:48–57. https://doi.org/10.1016/j.insmatheco.2017.04.004

Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468. https://doi.org/10.1016/j.energy.2018.01.177

Reddi SJ, Kale S, Kumar S (2018) On the convergence of Adam and beyond. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings

Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0

Shih SY, Sun FK, yi Lee H (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108(8-9):1421–1441. https://doi.org/10.1007/s10994-019-05815-0

Srivastava S, Lessmann S (2018) A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol Energy 162:232–247. https://doi.org/10.1016/j.solener.2018.01.005

Taieb SB, Atiya AF (2016) A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting. IEEE Transactions on Neural Networks and Learning Systems 27(1):62–76. https://doi.org/10.1109/TNNLS.2015.2411629

Tsay RS (2005) Analysis of Financial Time Series Second Edition

Wang Y, Shen Y, Mao S, Chen X, Zou H (2018) LASSO & LSTM Integrated Temporal Model for Short-term Solar Intensity Forecasting. IEEE Internet Things J., https://doi.org/10.1109/JIOT.2018.2877510

Yu XH, Chen GA, Cheng SX (1995) Dynamic Learning Rate Optimization of the Backpropagation Algorithm. IEEE Transactions on Neural Networks 6(3):669–677. https://doi.org/10.1109/72.377972

Zang H, Liu L, Sun L, Cheng L, Wei Z, Sun G (2020) Short-term global horizontal irradiance forecasting based on a hybrid cnn-lstm model with spatiotemporal correlations. Renew Energy 160:26–41. https://doi.org/10.1016/j.renene.2020.05.150. http://www.sciencedirect.com/science/article/pii/S0960148120308557

Zeiler MD (2012) ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701, 1212.5701

Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175. https://doi.org/10.1016/S0925-2312(01)00702-0