Abstract
In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia. The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems. The novelty of the proposed approach is that it employs only the historical values of the GHI itself as explanatory variables and a fast training algorithm (resilient-propagation). In terms of performance metrics, the rp-trained FFANNs provided better results than Quasi-Newton (bfg) algorithm trained FFANNs for almost all the studied combinations of the FFANN structure. It has been also shown that increasing the number of neurons per layer didn’t improve the performance. Medium structures with fast training algorithms are recommended.
Highlights
Hail region (Saudi Arabia) has a semi-arid climate where solar energy is abundant, while water needs to be pumped from relatively deep wells
Due to the new electricity pricing policy adopted in Saudi Arabia at the beginning of 2018, where the price of one KWh increased from 0.05SAR to 0.18SAR (1USD=3.75SAR), solar energy can constitute an alternative solution replacing classical electricity
The present paper focuses on forecasting the global horizontal irradiation (GHI) in Hail region using different structures of a Feed-Forward Artificial Neural Networks (ANNs) (FFANN) based only on the historical records of the GHI itself
Summary
Hail region (Saudi Arabia) has a semi-arid climate where solar energy is abundant, while water needs to be pumped from relatively deep wells. Due to the new electricity pricing policy adopted in Saudi Arabia at the beginning of 2018, where the price of one KWh increased from 0.05SAR to 0.18SAR (1USD=3.75SAR), solar energy can constitute an alternative solution replacing classical electricity. Predicting as accurately as possible the future amounts of solar energy is of high importance in designing stand-alone or grid-connected solar plants. Given the fact that solar energy resources measurement stations remain relatively expensive, one solution is the design of computerized numerical forecasters. An increasing interest in more reliable and accurate forecasting approaches has been observed among the solar energy research community during the last few years. Various techniques have been used in the literature to forecast
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