Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods

Robert Basmadjian,Amirhossein Shaafieyoun,Sahib Julka

doi:10.3390/en14217443

Robert Basmadjian, Amirhossein Shaafieyoun + Show 1 more

Open Access

https://doi.org/10.3390/en14217443

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Abstract

Forecasting renewable energy sources is of critical importance to several practical applications in the energy field. However, due to the inherent volatile nature of these energy sources, doing so remains challenging. Numerous time-series methods have been explored in literature, which consider only one specific type of renewables (e.g., solar or wind), and are suited to small-scale (micro-level) deployments. In this paper, the different types of renewable energy sources are reflected, which are distributed at a national level (macro-level). To generate accurate predictions, a methodology is proposed, which consists of two main phases. In the first phase, the most relevant variables having impact on the generation of the renewables are identified using correlation analysis. The second phase consists of (1) estimating model parameters, (2) optimising and reducing the number of generated models, and (3) selecting the best model for the method under study. To this end, the three most-relevant time-series auto-regression based methods of SARIMAX, SARIMA, and ARIMAX are considered. After deriving the best model for each method, then a comparison is carried out between them by taking into account different months of the year. The evaluation results illustrate that our forecasts have mean absolute error rates between 6.76 and 11.57%, while considering both inter- and intra-day scenarios. The best models are implemented in an open-source REN4Kast software platform.

Highlights

The walk-forward approach was used for testing: in each iteration, the model predicted the day ahead, and this day was added to the training set to forecast the day after recursively
The results show that the mean of the MAEs, the mean of the RMSEs, and the mean absolute of the Errors for these three days were 5.10%, 5.73%, and 4.31% respectively, which are even better than their counterparts in 2019
The percentage of renewables at the national level is studied by considering different types of renewable energy sources

Summary

Introduction

Renewables are intermittent in nature, and their volatility could lead to an imbalance between power generation and demand, which endangers the stability of the grid [1]. To circumvent this situation, there was a paradigm change from traditional “supply follows demand” to “demand-side management” (DSM) [2]. There was a paradigm change from traditional “supply follows demand” to “demand-side management” (DSM) [2] In this regard, the key aspect of DSM is to carry out short-term (e.g., day-ahead) planning and scheduling (e.g., when and how much power to feed-in from renewables or increase/decrease the demand) of the power system. We focus on generating short-term forecasts for renewables, due to thevlack of contributions in this respect on the one hand, and on the other forecasts for demand have been extensively and exhaustively studied in the literature [3,4,5,6,7,8] (Some of the recent publications)

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