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-kW(p) 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.