Accurate Prediction of Short-term Photovoltaic Power Generation via A Novel Double-Input-Rule-Modules Stacked Deep Fuzzy Method

Abstract Accurate prediction of the photovoltaic (PV) power generation is of great significance for the efficient management of the power grid. In order to strengthen the interpretability of the data-driven models for PV power prediction and to further improve the forecasting accuracy, a novel double-input-rule-modules (DIRMs) stacked deep fuzzy model (DIRM-DFM) is proposed in this study. Firstly, the proposed stacked structure of DIRM-DFM is presented. This novel modular structure adopts a bottom-up, layer-by-layer design scheme by stacking the DIRMs which has only two input variables. This scheme assures the interpretability of the proposed novel fuzzy model. Then, to guarantee the performance of DIRM-DFM, its learning mechanism, including the training data generation, the construction of the DIRMs, are given in detail. This learning mechanism has fast learning speed and excellent approximation ability, because each DIRM is optimized by the popular least square method. Finally, two real-world experiments for predicting the PV power generation are conducted to verify the proposed DIRM-DFM, and detailed comparisons are made with traditional and deep fuzzy models, shallow and deep neural networks. Experimental results clearly demonstrated that the proposed DIRM-DFM has the best accuracy and the reactively fast training speed while having the apparent advantages of interpretability.

[1]  Masaharu Mizumoto,et al.  On the Equivalence Conditions of Fuzzy Inference Methods—Part 1: Basic Concept and Definition , 2011, IEEE Transactions on Fuzzy Systems.

[2]  Jianqiang Yi,et al.  A new fuzzy controller for stabilization of parallel-type double inverted pendulum system , 2002, Fuzzy Sets Syst..

[3]  Nanrun Zhou,et al.  Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine , 2020, Energy.

[4]  Aysen Apaydin,et al.  A New Spatial Algorithm Based on Adaptive Fuzzy Neural Network for Prediction of Crustal Motion Velocities in Earthquake Research , 2018, Int. J. Fuzzy Syst..

[5]  Chengdong Li,et al.  A Hybrid Short-Term Building Electrical Load Forecasting Model Combining the Periodic Pattern, Fuzzy System, and Wavelet Transform , 2020, Int. J. Fuzzy Syst..

[6]  Hiroaki Ishii,et al.  On the Generalization of Single Input Rule Modules Connected Type Fuzzy Reasoning Method , 2008, IEEE Transactions on Fuzzy Systems.

[7]  Kejun Wang,et al.  Photovoltaic power forecasting based LSTM-Convolutional Network , 2019 .

[8]  Fang Liu,et al.  IET Renewable Power Generation Special Issue: Performance Assessment and Condition Monitoring of Photovoltaic Systems for Improved Energy Yield Takagi–Sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term forecasting , 2020 .

[9]  Jianqiang Yi,et al.  Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems , 2018, IEEE Transactions on Fuzzy Systems.

[10]  Masaharu Mizumoto,et al.  SIRMs connected fuzzy inference method adopting emphasis and suppression , 2013, Fuzzy Sets Syst..

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

[12]  Da Liu,et al.  Random forest solar power forecast based on classification optimization , 2019, Energy.

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

[14]  Jianqiang Yi,et al.  Anti-swing and positioning control of overhead traveling crane , 2003, Inf. Sci..

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

[16]  Sameer Al-Dahidi,et al.  Ensemble Approach of Optimized Artificial Neural Networks for Solar Photovoltaic Power Prediction , 2019, IEEE Access.

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

[18]  Arno Krenzinger,et al.  Degradation analysis of a photovoltaic generator after operating for 15 years in southern Brazil , 2020 .

[19]  A. Massi Pavan,et al.  A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant , 2013 .

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

[21]  H. Pedro,et al.  Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

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

[23]  Hisao Ishibuchi,et al.  Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules , 2018, IEEE Transactions on Fuzzy Systems.

[24]  Luis Fontan,et al.  Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control , 2018, Renewable Energy.

[25]  Ki-Hyun Kim,et al.  Solar energy: Potential and future prospects , 2018 .

[26]  Li-Xin Wang,et al.  Fast Training Algorithms for Deep Convolutional Fuzzy Systems With Application to Stock Index Prediction , 2018, IEEE Transactions on Fuzzy Systems.

[27]  Yugang Niu,et al.  Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM , 2018 .

[28]  Jianjing Li,et al.  Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM , 2019, Energy.

[29]  Thomas F. Coleman,et al.  A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables , 1992, SIAM J. Optim..

[30]  Yanping Bai,et al.  Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm , 2015, Neurocomputing.

[31]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[32]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[33]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[34]  Jianqiang Yi,et al.  Upswing and stabilization control of inverted pendulum system based on the SIRMs dynamically connected fuzzy inference model , 2001, Fuzzy Sets Syst..

[35]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[36]  Shiwen Tong,et al.  Control of a fuel cell based on the SIRMs fuzzy inference model , 2013 .

[37]  Hiroaki Ishii,et al.  On the Monotonicity of Fuzzy-Inference Methods Related to T–S Inference Method , 2010, IEEE Transactions on Fuzzy Systems.

[38]  Luca Massidda,et al.  Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany , 2017 .

[39]  J. Yi,et al.  SIRMS BASED INTERVAL TYPE-2 FUZZY INFERENCE SYSTEMS: PROPERTIES AND APPLICATION , 2010 .

[40]  Abdollah Ahmadi,et al.  A literature review on estimating of PV-array hourly power under cloudy weather conditions , 2016 .

[41]  Luca Delle Monache,et al.  Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble , 2017 .

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

[43]  Saad Mekhilef,et al.  Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems , 2017 .

[44]  Jesús Alcalá-Fdez,et al.  A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning , 2011, IEEE Transactions on Fuzzy Systems.

[45]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

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

[47]  Arnulf Jäger-Waldau,et al.  Snapshot of Photovoltaics—February 2020 , 2020, Energies.

[48]  Jean-Laurent Duchaud,et al.  Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability , 2018, Energy.

[49]  Giorgio Graditi,et al.  Comparison of Photovoltaic plant power production prediction methods using a large measured dataset , 2016 .

[50]  M. G. De Giorgi,et al.  Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine , 2016 .

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

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