At present, the method of predicting wind power generation is mainly based on data integration calculation. Although this method is simple, it has shortcomings in short-term and ultra-short-term predictions owing to low accuracy. In this study, the clustering analysis data processing method is used to pre-process the meteorological wind power generation data, thus improving the data quality. This method builds model samples based on historical data with similar numerical weather prediction (NWP) characteristic parameters of the original sample data and forecast date, takes the NWP information of the forecast date as the basis of similarity measurement, and extracts effective data for the neural network prediction model after the improved clustering processing. Therefore, short-term wind power prediction analysis can be performed. Herein, the proposed data processing method is combined with the neural network model to create a software product that is applied to a wind farm in northeast China. The combined clustering data processing method of the wind power prediction model improved power prediction by ∼12% compared with that of the traditional continuous model. This demonstrates an obvious improvement in the prediction accuracy, thereby further proving the validity of the proposed method.

Forecasting the output power of large wind farms is helpful for planning reserve capacity in a mixed-generation power supply system. After classifying the reserves needed in a power system, this research focuses on the reserve components that are affected by wind–power fluctuations, the regulating, and load-following reserves. Considering the uncertainties in system load forecasts and wind–power forecasts, a general model for calculating the reserve requirements of a power system is proposed. Then, ultra-short-term forecasts of wind–power output 1 h into the future were studied, and a practical method that uses 10-min data to forecast wind output with a rolling average was proposed to predict the amount of power generation in the next hour. Then, using the RTS-96 dataset from the IEEE as an example, the reserve capacity requirement for the power system is determined according to the forecast uncertainty. Accurate predictive models for wind forecasting can reduce the needed reserve capacity of the system while guaranteeing the reliability and safety of the grid.

In this study, a novel model of energy hub is proposed, which can promote the utilisation of clean energy and energy efficiency, and achieve the purpose of the local energy consumption. Secondly, based on the framework of interconnected energy hubs, call action is applied in energy internet. Then, a distributed energy management strategy is designed for energy hubs by consistency theory. Case studies show that the price adjustment mechanism is stable with more than 500 energy hubs and the price adjustment tends to a constant value after 15 times. Compared with traditional energy management, the management strategy proposed in this study is more beneficial for both surplus-hubs and short-hubs.

As an important part of a multi-energy system, the energy hub (EH) accommodates a wide range of input sources and diversified load types. Optimising the equipment type and capacity of the EH is the basis for ensuring the safe and economic operation of the EH. In this context, the basic concepts and models of the EH are first outlined. Then, to reduce the user's electricity cost and user comfort as the optimisation goal, the tie line power constraint and the purchase and sale power state constraints are considered comprehensively.

Wireless sensor networks consist of a collection of sensors to monitor physical or environmental events. Nowadays, the sensor networks are used in important applications like military, health and civilian monitoring. Since it is a wireless medium, deployed in remote locations and resource-constrained nature, the sensor networks are easily vulnerable to attacks. The attack creates significant damages to the sensor networks. To avoid these problems, intrusion detection system (IDS) is implemented at the base station to filter any abnormal packets. In the proposed system, a survey is made on the attacks and rules to detect the attacks. Filtering the attacks using rule-based IDS at the sensor nodes would reduce the amount of packet transmission to the base station which, in turn, would reduce the energy consumption of the sensor network. Extreme learning machine (ELM) algorithm is implemented at the base station to detect the abnormal packets. The experimental result shows the performance of different classification techniques and cross-layer rules over the NSL-KDD and real-time datasets. The detection rate of the ELM algorithm is higher compared to other systems.

The messages in vehicular ad hoc networks (VANET) are vulnerable to attack in the open wireless environment. Group communication in VANET is receiving more attention, which confronts many security issues. First, only users who have passed legal authentication can communicate in the group. The ring signature is an effective solution. In addition, the group membership in the vehicular self-organising network changes rapidly, which leads to dynamic adjustment of group members. It is necessary to consider the computing power and communication capability of each node in the group members. To speed up the authentication process, a roadside unit with more computing power and communication capability than vehicles is selected as the signature agent, which effectively reduces the computational load of vehicles and accelerates the communication throughput. Meanwhile, the computational task and communication overhead have not been well-solved. To address these issues, this paper proposed an efficient proxy ring signature scheme for VANET to achieve higher signature efficiency and lower transmission overhead. The scheme allows both privacy protection and original signer tracing. Security analysis shows that the proposed scheme meets the security requirements of VANET.

Replacing traditional building materials with renewable and sustainable materials such as timber can control environmental pollution such as energy consumption and carbon emission in the whole life cycle of a building, which is conducive to energy conservation and environmental protection. How to design timber structure building better is the focus of this study. This study takes timber structure building as the research object, evaluates the building energy consumption and CO₂ emission with the whole life cycle concept. The comparative ecological advantages of timber structure building are obtained by comparing and analysing the brick-concrete structure building with equivalent thermal performance. Then by analysing the different spaces of timber structure building design, the design suggestions of energy saving and carbon reduction such as reducing the platform area appropriately and increasing the number of building floors are proposed when designing timber structure buildings.