With the rapid development of IoT, big data and artificial intelligence, the research and application of data-driven hydrological models are increasing. However, when conducting time series analysis, many prediction models are often directly based on the following assumptions: hydrologic time series are normal, homogeneous, smooth and non-trending, which are not always all true. To address the related issues, a solution for short-term hydrological forecasting is proposed. Firstly, a feature test is conducted to verify whether the hydrological time series are normal, homogeneous, smooth and non-trending; secondly, a sequence-to-sequence (seq2seq)-based short-term water level prediction model (LSTM-seq2seq) is proposed to improve the accuracy of hydrological prediction. The model uses a long short-term memory neural network (LSTM) as an encoding layer to encode the historical flow sequence into a context vector, and another LSTM as a decoding layer to decode the context vector in order to predict the target runoff, by superimposing on the attention mechanism, aiming at improving the prediction accuracy. Using the experimental data regarding the water level of the Chu River, the model is compared to other models based on the analysis of normality, smoothness, homogeneity and trending of different water level data. The results show that the prediction accuracy of the proposed model is greater than that of the data set without these characteristics for the data set with normality, smoothness, homogeneity and trend. Flow data at Runcheng, Wuzhi, Baima Temple, Longmen Town, Dongwan, Lu’s and Tongguan are used as input data sets to train and evaluate the model. Metrics RMSE and NSE are used to evaluate the prediction accuracy and convergence speed of the model. The results show that the prediction accuracy of LSTM-seq2seq and LSTM-BP models is higher than other models. Furthermore, the convergence process of the LSTM-seq2seq model is the fastest among the compared models.

The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations. Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation. Therefore, to further study the time correlation for improving the prediction accuracy, an LSTM-FC deep learning algorithm composed of long-term short-term memory (LSTM) and fully connected (FC) layers is proposed. The double-branch input of the model enables it not only to consider the impact of meteorological data on power generation but also to consider time continuity and periodic dependence, thereby improving the prediction accuracy to a certain extent. In this paper, meteorological data, historical continuous data, and historical periodic data are used as experimental data, and these three types of data are combined into different input forms to evaluate and compare LSTM-FC with other baseline models, including support vector machines (SVM), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), feedforward neural network (FFNN), and LSTM. The simulation results show that the accuracy of the models with meteorological data, continuous data, and periodic data as input is higher than that of other input forms, and the accuracy of LSTM-FC is the highest among these models, and its root mean square error (RMSE) is 11.79% higher than that of SVM.

In premise of ensuring users' QoS, the paper proposes a novel load balancing mechanism in cloud computing environment. The idea takes into account not only the energy efficiency of cloud providers, also the needs of users' QoS. The main work includes: construct QoS model, resource model of cloud infrastructure and the mapping between low-level resource metrics and QoS attributes; model load status of the virtual machine instance, and estimate the resource utilization ratio of the virtual machine cluster quantitatively; design the task scheduling algorithm and elastic scaling algorithm to achieve the tasks distribution and the elastic scaling of the virtual machines cluster respectively. By simulation using CloudSim platform, the result shows that this novel method has better load balance degree and complement time compared with other common algorithms such as round robin and green load, achieving the purpose of optimal load balancing in premise of ensuring QoS.