Net load forecasts for solar-integrated operational grid feeders

Abstract This work proposes forecast models for solar-integrated, utility-scale feeders in the San Diego Gas & Electric operating region. The models predict the net load for horizons ranging from 10 to 30 min. The forecasting methods implemented include hybrid methods based on Artificial Neural Network (ANN) and Support Vector Regression (SVR), which are both coupled with image processing methods for sky images. These methods are compared against reference persistence methods. Three enhancement methods are implemented to further decrease forecasting error: (1) decomposing the time series of the net load to remove low-frequency load variation due to daily human activities; (2) segregating the model training between daytime and nighttime; and (3) incorporating sky image features as exogenous inputs in the daytime forecasts. The ANN and SVR models are trained and validated using six-month measurements of the net load and assessed using common statistic metrics: MBE, MAPE, rRMSE, and forecast skill, which is defined as the reduction of RMSE over the RMSE of reference persistence model. Results for the independent testing set show that data-driven models, with the enhancement methods, significantly outperform the reference persistence model, achieving forecasting skills (improvement over reference persistence model) as large as 43% depending on location, solar penetration and forecast horizons.

[1]  Carlos F.M. Coimbra,et al.  Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances , 2015 .

[2]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Jan Kleissl,et al.  Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego , 2014 .

[6]  D. Kirschen,et al.  Economic impact assessment of load forecast errors considering the cost of interruptions , 2006, 2006 IEEE Power Engineering Society General Meeting.

[7]  Ali Azadeh,et al.  An integrated artificial neural networks approach for predicting global radiation , 2009 .

[8]  C. Coimbra,et al.  Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database , 2011 .

[9]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[10]  Miltiadis Alamaniotis,et al.  Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting , 2012, IEEE Transactions on Power Systems.

[11]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[12]  Carlos F.M. Coimbra,et al.  Quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term DNI forecasts , 2016 .

[13]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[14]  Amanpreet Kaur,et al.  Impact of onsite solar generation on system load demand forecast , 2013 .

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

[16]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[17]  Jan Kleissl,et al.  High PV penetration impacts on five local distribution networks using high resolution solar resource assessment with sky imager and quasi-steady state distribution system simulations , 2016 .

[18]  Carlos F.M. Coimbra,et al.  Sun-tracking imaging system for intra-hour DNI forecasts , 2016 .

[19]  Charles M. Bachmann,et al.  Neural Networks and Their Applications , 1994 .

[20]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[21]  Carlos F.M. Coimbra,et al.  Real-time prediction intervals for intra-hour DNI forecasts , 2015 .

[22]  Anestis Antoniadis,et al.  A prediction interval for a function-valued forecast model: Application to load forecasting , 2014, 1412.4222.

[23]  Saleh M. Al-Alawi,et al.  An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation , 1998 .

[24]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[25]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[26]  Detlev Heinemann,et al.  Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts , 2015 .

[27]  R. Inman,et al.  Cloud enhancement of global horizontal irradiance in California and Hawaii , 2016 .

[28]  S. Nahavandi,et al.  Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts , 2013, IEEE Transactions on Sustainable Energy.

[29]  Johan A. K. Suykens,et al.  Load forecasting using a multivariate meta-learning system , 2013, Expert Syst. Appl..

[30]  Amanpreet Kaur,et al.  Ensemble re-forecasting methods for enhanced power load prediction , 2014 .

[31]  Carlos F.M. Coimbra,et al.  A Smart Image-Based Cloud Detection System for Intrahour Solar Irradiance Forecasts , 2014 .

[32]  Carlos F.M. Coimbra,et al.  Real-time forecasting of solar irradiance ramps with smart image processing , 2015 .

[33]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[34]  Carlos F.M. Coimbra,et al.  Short-term probabilistic forecasts for Direct Normal Irradiance , 2017 .

[35]  Carlos F.M. Coimbra,et al.  Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning , 2013 .

[36]  Jun Yang,et al.  A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images , 2011 .

[37]  C. Coimbra,et al.  Intra-hour DNI forecasting based on cloud tracking image analysis , 2013 .

[38]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[39]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[40]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[41]  R. Hanna,et al.  Impact Research of High Photovoltaics Penetration Using High Resolution Resource Assessment with Sky Imager and Power System Simulation , 2022 .

[42]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[43]  Paul Denholm,et al.  Evaluating the limits of solar photovoltaics (PV) in electric power systems utilizing energy storage and other enabling technologies , 2007 .

[44]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..