Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning

Abstract The outputs of photovoltaic (PV) power are random and uncertain due to the variations of meteorological elements, which may disturb the safety and stability of power system operation. Hence, precise day-ahead PV power forecasting is crucial in renewable energy utilization, as it is beneficial to power generation schedule and short-term dispatch of the PV integrated power grid. In this study, a novel day-ahead PV power forecasting approach based on deep learning is proposed and validated. Firstly, two novel deep convolutional neural networks (CNNs), i.e. residual network (ResNet) and dense convolutional network (DenseNet), are introduced as the core models of forecasting. Secondly, a new data preprocessing is proposed to construct input feature maps for the two novel CNNs, which involves historical PV power series, meteorological elements and numerical weather prediction. Thirdly, a meta learning strategy based on multi-loss-function network is proposed to train the two deep networks, which can ensure a high robustness of the extracted convolutional features. Owing to the learning strategy and unique architectures of the two novel CNNs, they are designed into relatively deep architectures with superb nonlinear representation abilities, which consist of more than ten layers. Both point and probabilistic forecasting results are provided in the case study, demonstrating the accuracy and reliability of the proposed forecasting approach.

[1]  Farhad Shahnia,et al.  Impact of Distributed Rooftop Photovoltaic Systems on Short-Circuit Faults in the Supplying Low Voltage Networks , 2017 .

[2]  Zhang Jianhua,et al.  Energy management system, generation and demand predictors: a review , 2018 .

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

[4]  Jianhua Zhang,et al.  A PSO-ANFIS based Hybrid Approach for Short Term PV Power Prediction in Microgrids , 2018 .

[5]  Adam R. Brandt,et al.  Solar PV output prediction from video streams using convolutional neural networks , 2018 .

[6]  Francisco J. Batlles,et al.  Online 3-h forecasting of the power output from a BIPV system using satellite observations and ANN , 2018, International Journal of Electrical Power & Energy Systems.

[7]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[9]  Tao Ding,et al.  Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China , 2019, Renewable Energy.

[10]  Iain MacGill,et al.  Comparative analysis of the variability of fixed and tracking photovoltaic systems , 2014 .

[11]  He Jiang,et al.  A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation , 2016 .

[12]  Andreas Svensson,et al.  Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes , 2018 .

[13]  Yitao Liu,et al.  Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network , 2017 .

[14]  Haiyan Lu,et al.  Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting , 2017 .

[15]  Jianming Hu,et al.  A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization , 2018, Applied Energy.

[16]  Wei Wu,et al.  Deep learning based on Batch Normalization for P300 signal detection , 2018, Neurocomputing.

[17]  Boris Hanin,et al.  Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients? , 2018, NeurIPS.

[18]  Carlos Rodríguez-Monroy,et al.  The Venezuelan energy crisis: Renewable energies in the transition towards sustainability , 2019, Renewable and Sustainable Energy Reviews.

[19]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[20]  Pengcheng Wei,et al.  Enhanced support vector regression based forecast engine to predict solar power output , 2018, Renewable Energy.

[21]  Younghoon Kim,et al.  Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks , 2018, IEEE Access.

[22]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[23]  E. D. Mehleri,et al.  Determination of the optimal tilt angle and orientation for solar photovoltaic arrays , 2010 .

[24]  Cheng Liu,et al.  Research and application of ensemble forecasting based on a novel multi-objective optimization algorithm for wind-speed forecasting , 2017 .

[25]  George Makrides,et al.  Forecasting degradation rates of different photovoltaic systems using robust principal component analysis and ARIMA , 2017 .

[26]  Yacine Rezgui,et al.  Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression , 2018, Energy.

[27]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

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

[29]  Sinvaldo Rodrigues Moreno,et al.  Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System , 2017, Renewable Energy.

[30]  Jian Xiao,et al.  Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression , 2018, IEEE Transactions on Industrial Electronics.

[31]  Bikash Kumar Sahu Wind energy developments and policies in China: A short review , 2018 .

[32]  Nicholas A. Engerer,et al.  Improved satellite-derived PV power nowcasting using real-time power data from reference PV systems , 2017, Solar Energy.

[33]  Chengshi Tian,et al.  A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting , 2019, Applied Energy.

[34]  N. Rahim,et al.  Solar photovoltaic generation forecasting methods: A review , 2018 .

[35]  The Distribution of Environmental Damages , 2019, Review of Environmental Economics and Policy.

[36]  Carlos F.M. Coimbra,et al.  History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining , 2018, Solar Energy.

[37]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Tao Ding,et al.  Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network , 2018, IET Generation, Transmission & Distribution.

[39]  Yanfei Li,et al.  Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network , 2018 .

[40]  T. V. Geetha,et al.  A meta-learning framework using representation learning to predict drug-drug interaction , 2018, J. Biomed. Informatics.

[41]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[42]  Dilek Uz Energy efficiency investments in small and medium sized manufacturing firms: The case of California energy crisis , 2017 .

[43]  R. Banerjee,et al.  Estimation of rooftop solar photovoltaic potential of a city , 2015 .

[44]  Qie Sun,et al.  Prediction of short-term PV power output and uncertainty analysis , 2018, Applied Energy.

[45]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Francisco J. Batlles,et al.  Solar radiation forecasting in the short- and medium-term under all sky conditions , 2015 .

[47]  Saad Mekhilef,et al.  Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques , 2019, IET Renewable Power Generation.

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

[49]  Carlos F.M. Coimbra,et al.  Short-term reforecasting of power output from a 48 MWe solar PV plant , 2015 .

[50]  Yang Wang,et al.  Exploring Key Weather Factors From Analytical Modeling Toward Improved Solar Power Forecasting , 2019, IEEE Transactions on Smart Grid.

[51]  Mathieu David,et al.  Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting , 2016 .

[52]  Mehdi Seyedmahmoudian,et al.  Short-term PV power forecasting using hybrid GASVM technique , 2019, Renewable Energy.

[53]  Yan Su,et al.  An ARMAX model for forecasting the power output of a grid connected photovoltaic system , 2014 .

[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]  Bri-Mathias Hodge,et al.  A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting , 2019, IEEE Transactions on Sustainable Energy.

[56]  Dan Keun Sung,et al.  Solar Power Prediction Based on Satellite Images and Support Vector Machine , 2016, IEEE Transactions on Sustainable Energy.

[57]  Ming-Lang Tseng,et al.  Renewable energy prediction: A novel short-term prediction model of photovoltaic output power , 2019, Journal of Cleaner Production.

[58]  Pasquale Marcello Falcone,et al.  The networking dynamics of the Italian biofuel industry in time of crisis: Finding an effective instrument mix for fostering a sustainable energy transition , 2018 .

[59]  Wenjie Zhang,et al.  An ensemble machine learning based approach for constructing probabilistic PV generation forecasting , 2017, 2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[60]  Jianzhou Wang,et al.  A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm , 2018 .

[61]  Asifullah Khan,et al.  Wind power prediction using deep neural network based meta regression and transfer learning , 2017, Appl. Soft Comput..

[62]  Pedro Rodriguez,et al.  Impact of 100-MW-scale PV plants with synchronous power controllers on power system stability in northern Chile , 2017 .

[63]  Haiyan Li,et al.  Probability density forecasting of wind power using quantile regression neural network and kernel density estimation , 2018 .

[64]  Ning Zhang,et al.  Probabilistic individual load forecasting using pinball loss guided LSTM , 2019, Applied Energy.