In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

Large workspace is one of the promising advantages possessed by the cable-driven parallel robots (CDPR) over the conventional rigid-link robots. This paper focuses on the dynamic analysis and workspace classification based on the general motion equation of cable robot and the unilateral property of cables. The combinations of different types of two conditions lead to several different types of workspace, including static equilibrium workspace, wrench closure workspace, wrench feasible workspace, dynamic workspace, and collision-free workspace. A qualitative comparison of different types of workspaces is performed. The simulation results verify the relationship between the several types of workspaces.