No abstract

The nonlinear characteristics of wind power series and random fluctuation characteristics of wind resources have a harmful effect on stability of wind power prediction. This paper proposes a novel hybrid wind power short-term prediction model for improving precision and stability of wind power prediction. Firstly, the non-stationary wind power time series is decomposed by complete ensemble empirical mode decomposition-Lempel-Ziv complexity (CEEMD-LZC). Secondly, local linear embedding (LLE) is used to reduce the dimension of meteorological data with maintaining the essential structure of meteorological data. The integrated meteorological data predigested by LLE are treated as input of the prediction model. Finally, ant lion optimization (ALO) is utilized to optimize the weights and biases of extreme learning machine (ELM) network nodes to improve ELM prediction performance. The results of case studies show that the proposed model has a good forecasting effect and is more advantageous in 48h-ahead wind power forecasting. INDEX TERMS Wind power prediction, complete ensemble empirical mode decomposition-Lempel-Ziv complexity, local linear embedding, extreme learning machine, ant lion optimization.

In order to reduce the impact of renewable energy output and load fluctuations and improve the flexibility of the integrated energy system (IES), it is necessary to further promote user participation in integrated demand response (IDR). Therefore, this paper constructs a hierarchical framework that enables the transaction of IDR resources among users by combining the information interaction network established by blockchain and the energy management network established by energy management system. This paper also analyses the comfort of users, the cost of energy purchase by users and the cost of energy use by load aggregators, and then develops a two-layer optimization model. The results of the simulation show that the model and trading framework constructed in this paper can realize the trading of IDR resources among users, which effectively promotes the participation of users in IDR, reduces the cost of users and load aggregators, reduces the loss of IDR resources, enables more effective integration of dispersed IDR resources and improves the flexibility of IES.

Unmanned Aerial Vehicles (UAVs) are a novel technology for landform investigations, monitoring, as well as evolution analyses of long−term repeated observation. However, impacted by the sophisticated topographic environment, fluctuating terrain and incomplete field observations, significant differences have been found between 3D measurement accuracy and the Digital Surface Model (DSM). In this study, the DJI Phantom 4 RTK UAV was adopted to capture images of complex pit-rim landforms with significant elevation undulations. A repeated observation data acquisition scheme was proposed for a small amount of oblique-view imaging, while an ortho-view observation was conducted. Subsequently, the 3D scenes and DSMs were formed by employing Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms. Moreover, a comparison and 3D measurement accuracy analysis were conducted based on the internal and external precision by exploiting checkpoint and DSM of Difference (DoD) error analysis methods. As indicated by the results, the 3D scene plane for two imaging types could reach an accuracy of centimeters, whereas the elevation accuracy of the orthophoto dataset alone could only reach the decimeters (0.3049 m). However, only 6.30% of the total image number of oblique images was required to improve the elevation accuracy by one order of magnitude (0.0942 m). (2) An insignificant variation in internal accuracy was reported in oblique imaging-assisted datasets. In particular, SfM-MVS technology exhibited high reproducibility for repeated observations. By changing the number and position of oblique images, the external precision was able to increase effectively, the elevation error distribution was improved to become more concentrated and stable. Accordingly, a repeated observation method only including a few oblique images has been proposed and demonstrated in this study, which could optimize the elevation and improve the accuracy. The research results could provide practical and effective technology reference strategies for geomorphological surveys and repeated observation analyses in sophisticated mountain environments.

No abstract