Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models

Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.

[1]  Francesco Grimaccia,et al.  Neuro-fuzzy predictive model for PV energy production based on weather forecast , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[2]  Francesco Grimaccia,et al.  Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power , 2017, Math. Comput. Simul..

[3]  Enes Gonçalves Marra,et al.  Analysis of inverter sizing ratio for PV systems considering local climate data in central Brazil , 2017 .

[4]  M. Russo,et al.  Genetic programming for photovoltaic plant output forecasting , 2014 .

[5]  Hitoshi Iba,et al.  Using genetic programming to predict financial data , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Cosmin Safta,et al.  Efficient Uncertainty Quantification in Stochastic Economic Dispatch , 2017, IEEE Transactions on Power Systems.

[7]  M. Diagne,et al.  Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .

[8]  Pandelis N. Biskas,et al.  Multiple Time Resolution Stochastic Scheduling for Systems With High Renewable Penetration , 2017, IEEE Transactions on Power Systems.

[9]  Richard Perez,et al.  Characterization of the intraday variability regime of solar irradiation of climatically distinct locations , 2016 .

[10]  Irena Koprinska,et al.  Univariate and multivariate methods for very short-term solar photovoltaic power forecasting , 2016 .

[11]  Miguel-Ángel Manso-Callejo,et al.  Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations , 2016 .

[12]  Serafín Martínez-Jaramillo,et al.  An Heterogeneous, Endogenous and Coevolutionary GP-Based Financial Market , 2009, IEEE Transactions on Evolutionary Computation.

[13]  Vladan Babovic,et al.  Data mining in hydrology , 2005 .

[14]  Vladimiro Miranda,et al.  Spatial-Temporal Solar Power Forecasting for Smart Grids , 2015, IEEE Transactions on Industrial Informatics.

[15]  Francesco Grimaccia,et al.  Artificial Intelligence Forecast of PV Plant Production for Integration in Smart Energy Systems , 2012 .

[16]  Ricardo J. Bessa,et al.  Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions , 2017, IEEE Transactions on Sustainable Energy.

[17]  P. Ineichen,et al.  A new airmass independent formulation for the Linke turbidity coefficient , 2002 .

[18]  H. Pedro,et al.  Short-term irradiance forecastability for various solar micro-climates , 2015 .

[19]  Mathieu David,et al.  Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting , 2016 .

[20]  Nima Amjady,et al.  Effective prediction model for Hungarian small-scale solar power output , 2017 .

[21]  Carlos A. Martinez,et al.  Conceptual Developments in Genetic Programming for Time Series Forecasting , 2015, IEEE Latin America Transactions.

[22]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems , 2011, Neural Computing and Applications.

[23]  Akhil Garg,et al.  Empirical analysis of model selection criteria for genetic programming in modeling of time series system , 2013, 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).

[24]  Eric Wai Ming Lee,et al.  Short-term prediction of photovoltaic energy generation by intelligent approach , 2012 .

[25]  V. Babovic,et al.  Forecasting of River Discharges in the Presence of Chaos and Noise , 2000 .

[26]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[27]  J. Kleissl,et al.  Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed , 2011 .

[28]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[29]  Lu Zhao,et al.  PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore , 2016 .