Stream analytics for utilities. Predicting power supply and demand in a smart grid

In today's networked society, getting knowledge from the data becomes in a key task for many support decisions making processes. Such kind of decisions could imply serious consequences in business and life and as more information is available about the problem, more accurate will be the decision. A largely untested hypothesis of modern society is that it is important to record data as it may contain valuable information. However, we are living in the information age and it has gone into how this quantity of data might be analyzed. The number of data sources increases, which makes harder and, in some cases, unfeasible the approach of storing every piece of data. Actually, the amount of available storage capacity could be not enough in order to record all the digital info generated in the world and this could become one the main triggers for a paradigm shift. Another aspect is that it is necessary to react instantly to meaningful changes in data and detect complex patterns over time. Data stream paradigm was born as an answer to the continuous data problem that cannot be stored and analyzed in efficient manner. Data Streams can handle bigger data sizes than memory, and can extend to challenging real-time applications not previously tackled by e.g. time series analysis, traditional, databases, machine learning and data mining techniques. The assumption is that processing examples can be processed on the fly, that is, as soon as they arrive within a high speed stream, and then, must be discarded in order to make room for subsequent data (e.g. examples in a learning scenario). In this article we will discuss the application of data stream techniques in the Smart Grid use case. The new smart grids will require real time reaction and make distribution decision very fast in order to cover unexpected demand peaks and renewable generation peaks.

[1]  Ricard Gavaldà,et al.  Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.

[2]  Ignacio J. Ramírez-Rosado,et al.  Next-day Wind Park Electric Energy Generation Forecasting using Fuzzy Time-series , 2003, Modelling, Identification and Control.

[3]  E. El-Saadany,et al.  Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.

[4]  Carlos R. Minussi,et al.  A fast electric load forecasting using adaptive neural networks , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[5]  Henrik Madsen,et al.  Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models , 2012 .

[6]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[7]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

[8]  R. E. Abdel-Aal,et al.  Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks , 2009 .

[9]  P. Dokopoulos,et al.  A fuzzy expert system for the forecasting of wind speed and power generation in wind farms , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[10]  Serhan Ozdemir,et al.  Wind speed time series characterization by Hilbert transform , 2006 .

[11]  Ahmad Lotfi,et al.  Application of learning fuzzy inference systems in electricity load forecast , 2002 .

[12]  C. Moehrlen.  Uncertainty in wind energy forecasting , 2004 .

[13]  J. Usaola,et al.  Benefits for Wind Energy in Electricity Markets from Using Short Term Wind Power Prediction Tools; A Simulation Study , 2004 .

[14]  Vladimiro Miranda,et al.  Very Short-Term Wind Power Forecasting: State-of-the-Art , 2014 .

[15]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[16]  欧州風力エネルギー協会,et al.  "Powering Europe", wind energy and the electricity grid , 2012 .

[17]  Theresa L. Utlaut,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[18]  George Stavrakakis,et al.  Advanced short-term forecasting of wind power production , 1997 .