EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models

Abstract

Abstract-Energy forecasting provides essential contribution to integrate renewable energy sources into power systems. Today, renewable energy from wind power is one of the fastest growing means of power generation. As wind power forecast accuracy gains growing significance, the number of models used for forecasting is increasing as well. In this paper, we propose an autoregressive (AR) model that can be used as a benchmark model to validate and rank different forecasting models and their accuracy. The presented paper and research was developed within the scope of the European energy market (EEM) 2017 wind power forecasting competition. I. INTRODUCTIONWind power is one of the fastest growing means of power generation. In a time of paradigm change in energy policy and intensified competition of renewable energy sources, wind power offers several environmental benefits. However, due to its intermittent nature, the integration of wind power poses challenges on power system operation [1], [2], [3]. In order to minimize power imbalance, appropriate means of forecasting power production are essential. Since forecasting is inherently erroneous, the target is to minimize the forecast error. A vast number of forecast models have been developed in research. The state-of-the-art in wind power forecasting is summarized in [1] and [4]. In the review of [5], five basic types of forecasting models are identified.Autoregressive (AR) and autoregressive moving average models (ARMA) have been widely used to predict the wind speed or the wind power generation directly. Reference [6] for example, provides a methodology to generate statistically dependent wind speed scenarios based on ARMA modeling. It is concluded that this methodology is accurate in reproducing wind speed historical series. In [7], an ARMA model is developed to predict wind speeds up to a forecast horizon of 10 hours. A different model is developed for each calendar month. Forecasts are proven to perform significantly better in short terms.A multi-variate ARMA model is presented in [8] for wind power generation forecasting and for simulation of realistic wind speed predictions with adjustable accuracy. In [9], the performance of an autoregressive model and a neural network (NN) model are compared based on the root mean square error. In their analysis, neural networks with a varying number of

Reviewing the impact of AI on renewable energy efficiency and management

“…These challenges include variability, uncertainty, and intermittency, which can affect the operational flexibility of power systems and the balancing of power generation (Lin et al, 2019;Kies et al, 2016). The integration of renewable power sources, which are not dispatchable but intermittent, also presents challenges for power system operation (Johnson et al, 2023;Dimoulkas et al, 2017). Furthermore, the intermittence of renewable energy resources poses a significant challenge to microgrid energy management systems (Owebor et Smart grids play a crucial role in addressing these challenges by enabling the efficient integration of renewable energy sources.…”