Abstract
Due to modern economies moving towards a more sustainable energy supply, solar power generation is becoming an area of paramount importance. In order to integrate this generated energy into the grid, solar irradiation must be forecasted, where deviations of the forecasted value involve significant costs. Intermittence, high frequency, and nonstationary are common features of solar irradiation data that have attracted the interest of numerous researchers from different disciplines. In fact, complex methods based on artificial intelligence have been typically used to address this problem. Nonetheless, adequate benchmarks have not been employed to justify such utilization. The objective of this work is to analyze the Holt–Winters (HW) method to forecast solar irradiation at short term (from 1 to 6 h). At the best of our knowledge, this methodology, which belongs to the family of the exponential smoothing methods and is widely used in industry applications ranging from supply chain demand forecasting to electricity load energy forecasting, has not been fully exploited in solar irradiation forecasting. Additionally, in case the accuracy achieved by the Holt–Winters method is not enough, still it can be utilized as a competitive benchmark given its feasible implementation. The Holt–Winters method performance is illustrated by forecasting two time series. Firstly, global horizontal irradiation (GHI) data have been chosen since forecasting GHI is a crucial part in systems based on photovoltaic (PV) energy conversion. Fluctuations of GHI due to passing clouds at short timescales (seconds and minutes) lead to high variability of power output from PV plants that can strain the grid due to voltage-flicker and balancing issues. Furthermore, direct normal irradiation (DNI) data are also forecasted given the role of main fuel that DNI plays in solar concentrator technologies such as solar thermal (CSP) and concentrated photovoltaic (CPV) power plants. Both solar irradiation data series have been hourly created from 1-minute irradiance measurements that were collected from ground-based weather stations located in Spain.
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Notes
- 1.
Although the seasonality period is 24 hours the whole year, we refer to differences of period to the number of hours during a day that solar irradiance is different from zero.
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The authors thank the ISFOC for kindly providing all the data used in this paper.
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Martín, A., Trapero, J.R. (2014). Recursive Estimation Methods to Forecast Short-Term Solar Irradiation. In: Lefebvre, G., Jiménez, E., Cabañas, B. (eds) Environment, Energy and Climate Change II. The Handbook of Environmental Chemistry, vol 34. Springer, Cham. https://doi.org/10.1007/698_2014_304
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DOI: https://doi.org/10.1007/698_2014_304
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