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.

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Natural gas consumption forecasting is crucial for transmission system operators, distribution system operators, traders, and other players in the market. This work collects natural gas forecasting scientific works in accordance with the forecasting tool used by Energinet, the Danish transmission system operator. The work provides an analytical description on the long-term stability and security of the natural gas transmission system in Denmark. This work offers a detailed scientific directory on natural gas forecasting, presenting the so far vaguely described market in a more structured manner. The paper was focused on presenting the latest findings on identifying the selection each time of the appropriate prognostic model for each application based on: ① the option for supporting double seasonality, ② various exogenous variables, ③ suitability for day-ahead forecasting, and ④ ease of use and all these versus Energinet’s current model.

Surface coatings of immobilized polymer brushes are used, e.g., as lubricants, for anti-fouling, and as adhesives. Based on surface-initiated controlled radical polymerization (SI-CRP), a fast, versatile, and enduring scaled SI-CRP (SI-CRP scaled ) approach for the formation of polymer brushes is reported. The chemical process is made from an easily prepared chemical solution that is reusable for more than 6 h, even in the presence of oxygen. Because an inert atmosphere is not required, the SI-CRP scaled process can be carried out under ambient conditions with no significant loss of polymerization activity and viability. The high bath life of the activated solution along with an extraordinarily high brush growth rate of 10 nm per minute makes this method industrially relevant. Based on a straightforward dip coating protocol, this is the first scaled polymer brush technique successfully demonstrated on an industrial scale, enabling uniform polymer brush-functionalized materials for a broader variety of applications.