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.
Highlights
In 2017, global energy investment declined, with a fall of 2% with respect to the previous year, mainly due to lower production of coal, hydroelectric, and nuclear power [1]
Renewables have shown unprecedented growth over the past few years because of a number of factors: climate change is a major concern worldwide [2], air pollution in big cities has become a serious problem [3], the depletion of conventional energy resources shows that, continuing the current pattern of management, they could be largely exhausted by the end of the century [4], the cost of electricity from wind power and photovoltaics is diminishing [5], and the deployment of renewable-based power plants requires the least time among all power generation technologies [6]
The forecasting methods are based on Feed-Forward Neural Networks (FFNNs), named
Summary
In 2017, global energy investment declined, with a fall of 2% with respect to the previous year, mainly due to lower production of coal, hydroelectric, and nuclear power [1]. Renewables have shown unprecedented growth over the past few years because of a number of factors: climate change is a major concern worldwide [2], air pollution in big cities (e.g. in China) has become a serious problem [3], the depletion of conventional energy resources shows that, continuing the current pattern of management, they could be largely exhausted by the end of the century [4], the cost of electricity from wind power and photovoltaics is diminishing [5], and the deployment of renewable-based power plants requires the least time among all power generation technologies [6] In this framework, photovoltaic is the fastest-growing renewable technology and the sector with the largest investment [6]. Accurate forecasting models based on the use of field
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