Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Abstract

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.Energies 2019, 12, 1621 2 of 15 measurements and data from weather forecast providers [7] are needed in order to optimize the operation of these systems. The benefits of such models are manifold. Considering the point of view of the owners of residential and Commercial-Industrial (C&I) PV plants, and specifically the possibility of operating in a grid-parity regime or better, accurate forecasts enable the maximization of the self-consumed energy and minimization of the Levelized Cost of the Energy produced (LCoE) [8,9]. Also, when residential and C&I systems operate as part of a microgrid, the power forecasts can be used as input for the Energy Management System (EMS) used to optimize the State of Charge (SoC) of the storage devices [10]. On the other hand, considering larger-scale plants and fuel parity [11], the managers of utility-scale PV plants use the forecasts to optimally plan the downtime of plants for maintenance purposes. Moreover, in countries with a day-ahead electricity market, forecast models allow for optimization of the schedule for the supply offers on the market, avoiding penalties and reduced revenues [12,13]. On the other hand, Distribution System Operators (DSOs) and Transmission System Operators (TSOs) also need reliable forecasts in order to handle the uncertainty and fluctuations of the PV distributed generators connected to their grids. The availability of reliable forecasts allows DSOs and TSOs to cope with the intermittent nature of PV plants, avoiding problems in balancing power generation and load demand [14], enhancing the stability of the system, and reducing the cost of ancillary services [15,16]. Accurate forecasts enhance reliability, and reduce costs by allowing effi...

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