Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis

Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, a review of the literature is presented on systems and forecasting approaches applied to the renewable energy domain. Next, a generic and large-scale renewable energy monitoring and forecast system based on intelligent data analysis is described. Finally, a genuine implementation of this system for wind energy is presented as a case study, together with its performance analysis results. This chapter stands as a significant reference for renewable energy informatics, considering the provided conceptual and applied system descriptions, heavily based on smart computing techniques. Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis

[1]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[2]  Xing Wang,et al.  Generation dispatch in a smart grid environment , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[3]  Pierre Pinson,et al.  Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy , 2016, IEEE Transactions on Power Systems.

[4]  Serkan Buhan,et al.  Multistage Wind-Electric Power Forecast by Using a Combination of Advanced Statistical Methods , 2015, IEEE Transactions on Industrial Informatics.

[5]  Henrik Madsen,et al.  Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts , 2006 .

[6]  Shin Ishii,et al.  Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Burcin Becerik-Gerber,et al.  Towards unsupervised learning of thermal comfort using infrared thermography , 2018 .

[8]  Mohamed Mohandes,et al.  Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[9]  Turan Demirci,et al.  Verification of a very short term wind power forecasting algorithm for Turkish transmission grid , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.

[10]  Stein-Erik Fleten,et al.  Short-term hydropower production planning by stochastic programming , 2008, Comput. Oper. Res..

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

[12]  Yakup S. Ozkazanç,et al.  Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power Forecasts , 2016, IEEE Transactions on Industrial Informatics.

[13]  Ivan Laptev,et al.  Is object localization for free? - Weakly-supervised learning with convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Dilek Küçük,et al.  Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis , 2017, DARE@PKDD/ECML.

[15]  Pinar Karagoz,et al.  A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP) , 2015, IEEE Transactions on Industrial Informatics.

[16]  J.M. Rodriguez,et al.  The integration of renewable energy and the system operation: The Special Regime Control Centre (CECRE) in Spain , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[17]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[18]  Luca Delle Monache,et al.  Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting , 2016 .

[19]  Francesco Palmieri,et al.  A study on forecasting electricity production and consumption in smart cities and factories , 2019, Int. J. Inf. Manag..

[20]  M. G. Lobo,et al.  Regional Wind Power Forecasting Based on Smoothing Techniques, With Application to the Spanish Peninsular System , 2012, IEEE Transactions on Power Systems.

[21]  Jie Zhang,et al.  LSTM-EFG for wind power forecasting based on sequential correlation features , 2019, Future Gener. Comput. Syst..

[22]  T. Horbury,et al.  Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or “Similar Day” Approach , 2017, Solar physics.

[23]  Joao P. S. Catalao,et al.  Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information , 2015 .

[24]  A. Khare.  Moderating Role of Demographics on Attitude towards Organic Food Purchase Behavior: A Study on Indian Consumers , 2017 .

[25]  Dilek Küçük,et al.  Verification of a real-time wind power monitoring and forecast system for Turkey , 2013 .

[26]  C. Ghanshyam,et al.  Fundamentals of Electrostatic Spraying: Basic Concepts and Engineering Practices , 2015 .

[27]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[28]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[29]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[30]  S. E. Haupt,et al.  A Wind Power Forecasting System to Optimize Grid Integration , 2012, IEEE Transactions on Sustainable Energy.

[31]  Turan Demirci,et al.  The architecture of a large-scale wind power monitoring and forecast system , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.

[32]  J. Kappenman,et al.  Management of the geomagnetically induced current risks on the national grid company's electric power transmission system , 2002 .

[33]  Ozgul Salor,et al.  Nationwide real-time monitoring system for electrical quantities and power quality of the electricity transmission system , 2011 .

[34]  Xin Yao,et al.  Resampling-Based Ensemble Methods for Online Class Imbalance Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.

[35]  Dilek Küçük,et al.  Enhanced Nationwide Wind-Electric Power Monitoring and Forecast System , 2014, IEEE Transactions on Industrial Informatics.