Profiling of prosumers for the needs of energy demand estimation in microgrids

Nowadays, a lot of attention in the smart grids' development is devoted to methods of estimation of the energy demand by taking into account the behavior of network participants (single prosumers and groups). However, these methods take an advantage from an analysis of the ex-post data, and as such they use mainly the data covering the energy consumption with no additional information about prosumers. The goal of this paper is to present and validate an ex-ante method for energy demand estimation based on profiling of prosumers, that enables estimation of the energy demand for each user stereotype, every hour, every day of the year and even for each device. The paper presents possible scenarios on how the proposed approach can be used for benefits of the microgrid.

[1]  C.X. Guo,et al.  Identification of fuzzy model for short-term load forecasting using evolutionary programming and orthogonal least squares , 2006, 2006 IEEE Power Engineering Society General Meeting.

[2]  Xiaodan Wang,et al.  Integration of Grey Model and Multiple Regression Model to Predict Energy Consumption , 2009, 2009 International Conference on Energy and Environment Technology.

[3]  Ugo Montanari,et al.  Real time market models and prosumer profiling , 2013, 2013 Proceedings IEEE INFOCOM.

[4]  Ioannis Lampropoulos,et al.  A methodology for modeling the behavior of electricity prosumers within the smart grid , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[5]  J. E. Payne,et al.  Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models , 2008 .

[6]  Akshay Kumar Saha,et al.  Application of adaptive network-based fuzzy inference system in short term load forecasting , 2007 .

[7]  Erkan Erdogdu Electricity Demand Analysis Using Cointegration and ARIMA Modelling: A case study of Turkey , 2007 .

[8]  Li Lin-rong,et al.  Short-term load combined forecasting method based on BPNN and LS-SVM , 2011, 2011 IEEE Power Engineering and Automation Conference.

[9]  M. B. Jain,et al.  Curve fitting and regression line method based seasonal short term load forecasting , 2012, 2012 World Congress on Information and Communication Technologies.

[10]  Agata Filipowska,et al.  Towards Forecasting Demand and Production of Electric Energy in Smart Grids , 2013, BIR.

[11]  A. Prudenzi,et al.  A Knowledge Based System for Medium Term Load Forecasting , 2006, 2005/2006 IEEE/PES Transmission and Distribution Conference and Exhibition.

[12]  Rong-Jong Wai,et al.  Design of intelligent long-term load forecasting with fuzzy neural network and particle swarm optimization , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[13]  Patrick P. K. Chan,et al.  Multiple classifier system for short term load forecast of Microgrid , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[14]  Tharam S. Dillon,et al.  Analysis of energy behaviour profiles of prosumers , 2012, IEEE 10th International Conference on Industrial Informatics.

[15]  Amal F. Abd El-Gawad,et al.  Applications on medium-term forecasting for loads and energy scales by using Artificial Neural Network , 2009 .

[16]  A. Al-Ghandoor,et al.  A multivariate linear regression model for the Jordanian industrial electric energy consumption , 2007 .

[17]  Tharam S. Dillon,et al.  Identifying prosumer's energy sharing behaviours for forming optimal prosumer-communities , 2011, 2011 International Conference on Cloud and Service Computing.

[18]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[19]  P. Damrongkulkamjorn,et al.  Monthly energy forecasting using decomposition method with application of seasonal ARIMA , 2005, 2005 International Power Engineering Conference.

[20]  Danny Chiang Choon Poo,et al.  A hybrid approach for user profiling , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[21]  Sy-Ruen Huang,et al.  Optimal identification of self-reunion multiple regression (SRMR) model based on regression function for short-term load forecasting , 2005, 2005 International Power Engineering Conference.

[22]  Carlo Gaetan,et al.  Subset ARMA Model Identification Using Genetic Algorithms , 2000 .