Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power.Temporal-spatial wind speed features contain rich information;however,their use to predict wind speed remains one of the most challenging and less studied areas.This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attentionbased long short-term memory(LSTM),termed 2Attn-LSTM,a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data.To eliminate the unevenness of the original wind speed,we initially decompose the preprocessing data into IMF components by variational mode decomposition(VMD).Then,it encodes the spatial features of IMF components at the bottom of the model and decodes the temporal features to obtain each component's predicted value on the second layer.Finally,we obtain the ultimate prediction value after denormalization and superposition.We have performed extensive experiments for short-term predictions on real-world data,demonstrating that 2Attn-LSTM outperforms the four baseline methods.It is worth pointing out that the presented 2Atts-LSTM is a general model suitable for other spatial-temporal features.

Effects of Virtual-real fusion on immersion,presence,and learning performance in laboratory education

Background Virtual-reality(VR)fusion techniques have become increasingly popular in recent years,and several previous studies have applied them to laboratory education.However,without a basis for evaluating the effects of virtual-real fusion on VR in education,many developers have chosen to abandon this expensive and complex set of techniques.Methods In this study,we experimentally investigate the effects of virtual-real fusion on immersion,presence,and learning performance.Each participant was randomly assigned to one of three conditions:a PC environment(PCE)operated by mouse;a VR environment(VRE)operated by controllers;or a VR environment running virtual-real fusion(VR VRFE),operated by real hands.Results The analysis of variance(ANOVA)and t-test results for presence and self-efficacy show significant differences between the PCE*VR-VRFE condition pair.Furthermore,the results show significant differences in the intrinsic value of learning performance for pairs PCE*VR VRFE and VRE*VR-VRFE,and a marginally significant difference was found for the immersion group.Conclusions The results suggest that virtual-real fusion can offer improved immersion,presence,and self efficacy compared to traditional PC environments,as well as a better intrinsic value of learning performance compared to both PC and VR environments.The results also suggest that virtual-real fusion offers a lower sense of presence compared to traditional VR environments.