Prediction granules for uncertainty modelling

In this paper, the concept of prediction granules (PGs) is introduced for the real world application problems. The PGs are constructed by prediction intervals (PIs) and a learning-based method. Specifically, a granular emotional neural network (GENN) is proposed and the resulting network is examined on real world wind farm power generation dataset, obtained from New South Wales of Australia. A traditional artificial neural network (ANN) is also applied for comparison purposes. Numerical results indicate that PGs can improve the prediction results and can provide useful information for prediction tasks of real world uncertain data.

[1]  Vera Chung,et al.  Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization , 2014 .

[2]  Baher Abdulhai,et al.  Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm , 2012, Eng. Appl. Artif. Intell..

[3]  R. Dolan,et al.  Knowing how much you don't know: a neural organization of uncertainty estimates , 2012, Nature Reviews Neuroscience.

[4]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Xiaofeng Meng,et al.  Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction , 2014, IEEE Transactions on Power Systems.

[6]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[7]  Saeid Nahavandi,et al.  An optimized mean variance estimation method for uncertainty quantification of wind power forecasts , 2014 .

[8]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[9]  Ehsan Lotfi,et al.  A winner-take-all approach to emotional neural networks with universal approximation property , 2015, Inf. Sci..

[10]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

[11]  Joseph E LeDoux.  Emotion circuits in the brain. , 2009, Annual review of neuroscience.

[12]  Peng Kou,et al.  Probabilistic wind power forecasting with online model selection and warped gaussian process , 2014 .

[13]  Yongqian Liu,et al.  Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine , 2013 .

[14]  Giacomo Capizzi,et al.  Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[16]  Ehsan Lotfi,et al.  Supervised brain emotional learning , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[17]  Sumit Saroha,et al.  Multi step ahead forecasting of wind power by different class of neural networks , 2014, 2014 Recent Advances in Engineering and Computational Sciences (RAECS).

[18]  Alan F. Murray,et al.  Confidence estimation methods for neural networks : a practical comparison , 2001, ESANN.

[19]  Stefano Alessandrini,et al.  A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data , 2013 .

[20]  Adnan Khashman,et al.  Modeling cognitive and emotional processes: A novel neural network architecture , 2010, Neural Networks.

[21]  M. Negnevitsky,et al.  Short term wind power forecasting using hybrid intelligent systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[22]  Ehsan Lotfi,et al.  Emotional Brain-Inspired Adaptive Fuzzy Decayed Learning for online prediction problems , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[23]  Ekaterina Mangalova,et al.  Wind power forecasting using the k-nearest neighbors algorithm , 2014 .

[24]  Henry Leung,et al.  Prediction Intervals for a Noisy Nonlinear Time Series Based on a Bootstrapping Reservoir Computing Network Ensemble , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Fang Liu,et al.  Short-Term Load Forecasting using Interval Arithmetic Backpropagation Neural Network , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[26]  T. Dalgleish.  The emotional brain , 2004, Nature Reviews Neuroscience.

[27]  Joao P. S. Catalao,et al.  Short-term wind power forecasting in Portugal by neural networks and wavelet transform , 2011 .

[28]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.

[29]  Adnan Khashman,et al.  Application of an emotional neural network to facial recognition , 2009, Neural Computing and Applications.

[30]  Ehsan Lotfi,et al.  Practical emotional neural networks , 2014, Neural Networks.

[31]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[32]  Robin Girard,et al.  Assessment of wind power predictability as a decision factor in the investment phase of wind farms , 2013 .

[33]  Ehsan Lotfi,et al.  BRAIN EMOTIONAL LEARNING-BASED PATTERN RECOGNIZER , 2013, Cybern. Syst..

[34]  Witold Pedrycz,et al.  Granular Neural Networks: Concepts and Development Schemes , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[36]  Wei-Jen Lee,et al.  An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty , 2007, IEEE Transactions on Industry Applications.

[37]  Babak Nadjar Araabi,et al.  Implementation of Emotional Controller for Interior Permanent Magnet Synchronous Motor Drive , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[38]  R. Elliott,et al.  Dissociable functions in the medial and lateral orbitofrontal cortex: evidence from human neuroimaging studies. , 2000, Cerebral cortex.

[39]  Farshid Keynia,et al.  Short-term wind power forecasting using ridgelet neural network , 2011 .

[40]  Z. A. Garczarczyk.  Interval neural networks , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[41]  Abraham Kandel,et al.  Granular neural networks for numerical-linguistic data fusion and knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[42]  George Galanis,et al.  Wind power prediction based on numerical and statistical models , 2013 .

[43]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[44]  Irena Koprinska,et al.  Electricity load forecasting based on autocorrelation analysis , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[45]  Abbas Khosravi,et al.  Construction of neural network-based prediction intervals using particle swarm optimization , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[46]  Amir F. Atiya,et al.  Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.

[47]  Adnan Khashman,et al.  A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients , 2008, IEEE Transactions on Neural Networks.

[48]  H. Bludszuweit,et al.  A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty , 2011, IEEE Transactions on Power Systems.

[49]  Ioannis B. Theocharis,et al.  Locally recurrent neural networks for wind speed prediction using spatial correlation , 2007, Inf. Sci..

[50]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[51]  P. Petta,et al.  Computational models of emotion , 2010 .

[52]  Hui Li,et al.  Research and Development of Granular Neural Networks , 2013 .

[53]  Saeid Nahavandi,et al.  Construction of neural network-based prediction intervals for short-term electrical load forecasting , 2013, 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG).

[54]  H. Critchley,et al.  A common role of insula in feelings, empathy and uncertainty , 2009, Trends in Cognitive Sciences.

[55]  Vladimiro Miranda,et al.  Time-adaptive quantile-copula for wind power probabilistic forecasting , 2012 .

[56]  Pedro Antonio Gutiérrez,et al.  Hyperbolic Tangent Basis Function Neural Networks Training by Hybrid Evolutionary Programming for Accurate Short-Term Wind Speed Prediction , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[57]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[58]  J. Eccles.  The emotional brain. , 1980, Bulletin et memoires de l'Academie royale de medecine de Belgique.

[59]  Ehsan Lotfi,et al.  Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices , 2014, Neurocomputing.

[60]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[61]  Maria Grazia De Giorgi,et al.  Assessment of the benefits of numerical weather predictions in wind power forecasting based on stati , 2011 .

[62]  Konrad Kording,et al.  Annals of the New York Academy of Sciences Bayesian Models: the Structure of the World, Uncertainty, Behavior, and the Brain , 2022 .

[63]  Haizhou Li,et al.  Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[64]  Caro Lucas,et al.  Learning based brain emotional intelligence as a new aspect for development of an alarm system , 2008, Soft Comput..

[65]  L. S. Moulin,et al.  Confidence intervals for neural network based short-term load forecasting , 2000 .

[66]  J. A. Mulder,et al.  Neural Network Output Optimization Using Interval Analysis , 2009, IEEE Transactions on Neural Networks.

[67]  Oscar Castillo,et al.  A new approach for time series prediction using ensembles of ANFIS models , 2012, Expert Syst. Appl..

[68]  A. Lüthi,et al.  Processing of Temporal Unpredictability in Human and Animal Amygdala , 2007, The Journal of Neuroscience.

[69]  Saeid Nahavandi,et al.  Prediction Interval Construction and Optimization for Adaptive Neurofuzzy Inference Systems , 2011, IEEE Transactions on Fuzzy Systems.

[70]  Khashayar Khorasani,et al.  New training strategies for constructive neural networks with application to regression problems , 2004, Neural Networks.

[71]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[72]  Georges Kariniotakis,et al.  Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[73]  Ahmed M. El-Garhy,et al.  PSO-BELBIC scheme for two-coupled distillation column process , 2011 .

[74]  C. Lucas,et al.  Emotional controller (BELBIC) for electric drives — A review , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[75]  Paras Mandal,et al.  A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.

[76]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[77]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[78]  Maria Grazia De Giorgi,et al.  Error analysis of short term wind power prediction models , 2011 .

[79]  Hongjie Jia,et al.  Granular neural networks , 2014, Artificial Intelligence Review.

[80]  Goran Pejanović,et al.  Improved wind forecasts for wind power generation using the Eta model and MOS (Model Output Statistics) method , 2014 .

[81]  Joseph E. LeDoux,et al.  Emotion and the limbic system concept , 1991 .

[82]  Panida Jirutitijaroen,et al.  Short-term load forecasting using time series analysis: A case study for Singapore , 2010, 2010 IEEE Conference on Cybernetics and Intelligent Systems.

[83]  H. Ishibuchi,et al.  An architecture of neural networks with interval weights and its application to fuzzy regression analysis , 1993 .

[84]  G. McCarthy,et al.  Decisions under Uncertainty: Probabilistic Context Influences Activation of Prefrontal and Parietal Cortices , 2005, The Journal of Neuroscience.

[85]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[86]  G. P. Pavlos,et al.  First and second order non-equilibrium phase transition and evidence for non-extensive Tsallis statistics in Earth’s magnetosphere , 2011 .

[87]  Hisao Ishibuchi,et al.  Improving the generalization ability of neural networks by interval arithmetic , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[88]  Alfredo Vaccaro,et al.  An adaptive framework based on multi-model data fusion for one-day-ahead wind power forecasting , 2011 .