Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods

Abstract

In this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregressive Integrated Moving Average (ARIMA), Trigonometric Seasonal Box-Cox Transformation with ARMA residuals Trend and Seasonal Components (TBATS) and Artificial Neural Networks (ANN) methods are used and seasonal naïve forecast is utilized as a benchmark. Mean absolute error (MAE) and root mean squared error (RMSE) are used as accuracy criterions. ARIMA and ANN are utilized with external variables and variable analysis is realized in order to improve forecasting results. As a result of variable analysis, it was observed that excluding temperature from external variables helped improve forecasting results. In terms of mean error ARIMA yielded the best results while ANN had the lowest minimum error and standard deviation. TBATS performed better than ANN in terms of mean error. To further improve forecasting accuracy, the three forecasts were combined using simple averaging and ANN methods and they were both found to be beneficial, with simple averaging having better accuracy. Overall, this paper demonstrates a solid forecasting methodology, while showing actual forecasting results and improvements for different forecasting methods.

Optimal operations of energy storage systems in multi‐application scenarios of grid ancillary services based on electricity price forecasting

“…Based on the implicit time dependence and the recursive relationship between electricity prices at different moments, time series are analysed to obtain short-term forecast electricity prices, mainly the widely used time series methods, including AR, GARCH 15 and ARIMAX. 16 However, statistical methods often have linear deviations, which makes it impossible to forecast nonlinear electricity price behaviour and difficult to respond to rapid fluctuations in price signals. 17,18 1.3 | Data-driven electricity price forecasting method…”

“…The electricity price of the peaking shaving is simulated according to the "Northeast Electric Power Ancillary Service Market Operation Rules (Trial)" at 58.82 USD/MWh, and the electricity price of the voltage regulation and standby market are adopted according to the standard of the "North China Regional Grid-connected Power Plant Ancillary Service Management Implementation Rules (Trial)," the capacity of the voltage regulation is 0.0147 USD/MWh and the electricity price of the spinning reserve is 1.47 USD/MWh. According to Equations (16) to (28), the marginal electricity price under different prediction methods is regarded as the input, and the simulation results are obtained as shown in Figure 9. The curve of the power and SOC of the ESS are shown in Figure 10.…”

How energy innovation in indoor vertical farming can improve food security, sustainability, and food safety?

“…Additional roles in the vertical farm systems towards increasing their efficiency have the connectivity with resourceful batteries that provide the opportunity for smart use of cheap stored electricity from the hours that the electricity prices are lower. An approach gaining constantly more and more attention also under the dynamic pricing concept, where also accurate forecasting plays a crucial role ( Karabiber and Xydis, 2019 ).…”

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

“…The result of this study show its superiority in performance compared to other benchmark methods, making it an effective method for analyzing and predicting carbon price. In Denmark, Karabiber and Xydis (2019) forecasted electricity prices for the Denmark-West region by applying the Autoregressive Integrated Moving Average (ARIMA), Trend and Seasonal Components (TBATS), and Artificial Neural Networks (ANN). In order to improve forecasting results, the study excluded temperature from the analysis.…”