Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks

As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long–short-term memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[3]  Dimitrios Soudris,et al.  A method for detailed, short-term energy yield forecasting of photovoltaic installations , 2019, Renewable Energy.

[4]  Bhartia,et al.  Aerosol Index from Toms and Omi Measurements , 2022 .

[5]  V. Piuri,et al.  Statistical models approach for solar radiation prediction , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[6]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[7]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[8]  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).

[9]  Wei Qiao,et al.  Short-term solar power prediction using a support vector machine , 2013 .

[10]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[11]  Kok Soon Tey,et al.  Forecasting of photovoltaic power generation and model optimization: A review , 2018 .

[12]  Giuseppe Marco Tina,et al.  Energy yield estimation of thin-film photovoltaic plants by using physical approach and artificial neural networks , 2016 .

[13]  Na Li,et al.  Solar generation prediction using the ARMA model in a laboratory-level micro-grid , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[14]  Younghoon Kim,et al.  Interactive Sleep Stage Labelling Tool For Diagnosing Sleep Disorder Using Deep Learning , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[16]  Chunxiang Yang,et al.  An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data , 2015, IEEE Transactions on Sustainable Energy.

[17]  Sue Ellen Haupt,et al.  Solar Forecasting: Methods, Challenges, and Performance , 2015, IEEE Power and Energy Magazine.

[18]  Ilhami Colak,et al.  Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[19]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[20]  M. Diagne,et al.  Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .

[21]  Emad M. Ahmed,et al.  PV power forecasting using different Artificial Neural Networks strategies , 2014, 2014 First International Conference on Green Energy ICGE 2014.

[22]  Lei Wang,et al.  An ANN-based Approach for Forecasting the Power Output of Photovoltaic System , 2011 .

[23]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[24]  Zeyar Aung,et al.  Probabilistic Forecasting of Solar Power: An Ensemble Learning Approach , 2017, KES-IDT.

[25]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[26]  Peng Wang,et al.  Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2011, IEEE Transactions on Industry Applications.

[27]  Mohamed Abuella,et al.  Solar power probabilistic forecasting by using multiple linear regression analysis , 2015, SoutheastCon 2015.

[28]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[29]  Isha,et al.  Solar Power Forecasting: A Review , 2016 .

[30]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[31]  Elnaz Jahani Heravi,et al.  Guide to Convolutional Neural Networks , 2017 .

[32]  Mohamed Abdel-Nasser,et al.  Accurate photovoltaic power forecasting models using deep LSTM-RNN , 2017, Neural Computing and Applications.

[33]  Chao Wu,et al.  DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[36]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[37]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[38]  Gordon Reikard.  Predicting solar radiation at high resolutions: A comparison of time series forecasts , 2009 .

[39]  Rongzhi Li,et al.  Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting , 2007 .

[40]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[41]  Yoshua Bengio,et al.  Unitary Evolution Recurrent Neural Networks , 2015, ICML.

[42]  Emanuele Crisostomi,et al.  Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants , 2018, IEEE Transactions on Sustainable Energy.

[43]  Junliang Liu,et al.  Convolutional neural networks for time series classification , 2017 .

[44]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[45]  Irena Report.  Renewable Power Generation Costs in 2012: An Overview , 2012 .

[46]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.