A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites

Roof-top solar photovoltaic systems are normally invisible to system operators, meaning that their generated power is not monitored. If a significant number of systems are installed, invisible solar power could significantly alter the net load in power systems. In this paper, a data-driven methodology is proposed to estimate the power generation of invisible solar power sites by using the measured values from a small number of representative sites. The proposed methodology is composed of a data dimension reduction engine and a mapping function. A number of established methods for reducing the dimension of large-scale data is investigated, and a hybrid method based on k -means clustering and principal component analysis is proposed. The output of this block provides a small subset of sites whose measured data are used in the mapping function. We have implemented several mapping functions to estimate the total generation power of all sites based on the measured output of the selected subset of sites. Numerical results based on data from California's power system are presented.

[1]  Ajay M. Patrikar.  Approximating Gaussian Mixture Model or Radial Basis Function Network With Multilayer Perceptron , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Le Xie,et al.  Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis , 2014, IEEE Transactions on Power Systems.

[3]  J. Lobry,et al.  Optimal Wind Clustering Methodology for Adequacy Evaluation in System Generation Studies Using Nonsequential Monte Carlo Simulation , 2011, IEEE Transactions on Power Systems.

[4]  Xin Yan,et al.  Linear Regression Analysis: Theory and Computing , 2009 .

[5]  Edgar N. Sánchez,et al.  Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[6]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[7]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[8]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[9]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[10]  Hamid Shaker,et al.  On comparison of two strategies in net demand forecasting using Wavelet Neural Network , 2014, 2014 North American Power Symposium (NAPS).

[11]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .

[12]  Edward K. Blum,et al.  Approximation theory and feedforward networks , 1991, Neural Networks.

[13]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[14]  Irena Koprinska,et al.  Correlation and instance based feature selection for electricity load forecasting , 2015, Knowl. Based Syst..

[15]  Ying Chen,et al.  Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks , 2010, IEEE Transactions on Power Systems.

[16]  I. Jolliffe.  Principal Component Analysis , 2002 .

[17]  Farshid Keynia,et al.  Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network , 2010 .

[18]  Dorra Sellami Masmoudi,et al.  Feature selection in possibilistic modeling , 2015, Pattern Recognit..

[19]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[20]  Hamidreza Zareipour,et al.  Data Mining for Electricity Price Classification and the Application to Demand-Side Management , 2012, IEEE Transactions on Smart Grid.

[21]  Pierre Hansen,et al.  NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.

[22]  Enrico Zio,et al.  Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[24]  Zengping Wang,et al.  PCA Fault Feature Extraction in Complex Electric Power Systems , 2010 .