Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron

Penetration of grid-connected photovoltaic systems can be increased substantially by devising area-specific power output forecasting methods. Meteorological conditions of the area are decisive for solar plant management and electricity generation. This paper estimates and forecasts the profile of power output of a grid-connected 20-kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India, using artificial neural networks (ANNs). A multilayer perceptron-based ANN model is proposed for day-ahead forecasting of the power generation. An experimental database comprising of each day’s solar power output and atmospheric temperature for a period of 70 days has been used for training the ANN. Various training algorithms, transfer functions, and learning rules in the hidden layers/output layers were employed on the database of 11,200 patterns in order to obtain the best mapping between the ANN’s inputs and outputs. Statistical error analysis in terms of mean absolute percentage error calculated on the 24-h-ahead forecasting results is presented. Analysis of the variations in network forecasting performance caused by changing the neuron functional parameters has been carried out. The results are also utilized for load scheduling operations of the industrial grid for the next day. Reliable area-specific solar power production map can help in power system scheduling and investment productivity.

[1]  M. Ranjan,et al.  Solar resource estimation using artificial neural networks and comparison with other correlation models , 2003 .

[2]  Jorge Aguilera,et al.  A new approach for sizing stand alone photovoltaic systems based in neural networks , 2005 .

[3]  Tariq Muneer,et al.  Sustainable production of solar electricity with particular reference to the Indian economy , 2005 .

[4]  Badia Amrouche,et al.  Artificial neural network based daily local forecasting for global solar radiation , 2014 .

[5]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.

[6]  Chao-Ming Huang,et al.  A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output , 2014, IEEE Transactions on Sustainable Energy.

[7]  A. Mellit,et al.  A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy , 2010 .

[8]  Wei Qiao,et al.  Short-term solar power prediction using a support vector machine , 2013 .

[9]  T. Muneer,et al.  Energy supply, its demand and security issues for developed and emerging economies , 2007 .

[10]  E. Izgi,et al.  Short–mid-term solar power prediction by using artificial neural networks , 2012 .

[11]  Prabhat,et al.  Artificial Neural Network , 2018, Encyclopedia of GIS.

[12]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for sizing photovoltaic systems: A review , 2009 .

[13]  Ramesh C. Bansal,et al.  A review of key power system stability challenges for large-scale PV integration , 2015 .

[14]  Kinattingal Sundareswaran,et al.  Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India) , 2014 .

[15]  Luis Gerardo Arriaga,et al.  A hybrid power plant (Solar–Wind–Hydrogen) model based in artificial intelligence for a remote-housing application in Mexico , 2013 .

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

[17]  Hongxing Yang,et al.  Solar photovoltaic system modeling and performance prediction , 2014 .

[18]  Montserrat Mendoza-Villena,et al.  Short-term power forecasting system for photovoltaic plants , 2012 .

[19]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[20]  Jeffrey M. Gordon,et al.  Analytical modeling of direct steam generation solar power plants , 2013 .

[21]  Jose C. Principe,et al.  Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM , 1999 .

[22]  Chul-Hwan Kim,et al.  Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[23]  Young Seok Jung,et al.  Performance results and analysis of 3kW grid-connected PV systems , 2007 .

[24]  B. Hodge,et al.  The value of day-ahead solar power forecasting improvement , 2016 .

[25]  Cyril Voyant,et al.  Statistical parameters as a means to a priori assess the accuracy of solar forecasting models , 2015 .

[26]  D. Elizondo,et al.  Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules , 2013 .

[27]  Ali Rahimikhoob,et al.  Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment , 2010 .

[28]  Eduardo F. Fernández,et al.  A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator , 2014 .

[29]  Jorge Aguilera,et al.  Generation of hourly irradiation synthetic series using the neural network multilayer perceptron , 2002 .

[30]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[31]  Sue Ellen Haupt,et al.  Solar Forecasting: Methods, Challenges, and Performance , 2015, IEEE Power and Energy Magazine.

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

[33]  Adel A. Ghoneim,et al.  Optimizing electrical load pattern in Kuwait using grid connected photovoltaic systems , 2004 .

[34]  Soteris A. Kalogirou,et al.  Artificial neural network-based model for estimating the produced power of a photovoltaic module , 2013 .