With the widespread use of the Internet, network security issues have attracted more and more attention, and network intrusion detection has become one of the main security technologies. As for network intrusion detection, the original data source always has a high dimension and a large amount of data, which greatly influence the efficiency and the accuracy. Thus, both feature selection and the classifier then play a significant role in raising the performance of network intrusion detection. This paper takes the results of classification optimization of weighted K-nearest neighbor (KNN) with those of the feature selection algorithm into consideration, and proposes a combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN, in order to improve the performance of network intrusion detection. Experimental results show that the weighted KNN can increase the efficiency at the expense of a small amount of the accuracy. Thus, the proposed combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN can then improve both the efficiency and the accuracy of network intrusion detection.

The increasing number of electric vehicles is forcing new solutions in the field of charging infrastructure. One such solution is photovoltaic carports, which have a double task. Firstly, they enable the generation of electricity to charge vehicles, and secondly, they protect the vehicle against the excessive heating of its interior. This article presents the functioning of a small carport for charging an electric vehicle. Attention is drawn to the problems of selecting the peak power of the photovoltaic system for charging an electric vehicle. An economic and energy analysis is carried out for the effective use of photovoltaic carports. In this article, we present the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of probability distribution.

This article presents a method for assessing the selection of carport power for an electric vehicle using the Metalog probability distribution family. Carports are used to generate electricity and provide shade for vehicles parked underneath them. On the roof of the carport, there is a photovoltaic system consisting of photovoltaic panels and an inverter. An inverter with Internet of Things functions generates data packets which describe the operation of the entire system at certain intervals and sends them via wireless transmission to a cloud server. The transmitted data can be processed offline and used to determine the charging capacity of individual electric vehicles. This article presents the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of the probability distribution. Based on the calculations, an electric vehicle was selected that can be charged from the carport.

Zero-emission transport is a very important topic that is increasingly taken up by many institutions and research centers around the world. However, the zero-emissivity of the vehicle is quite a complex issue, which should be understood as not only the lack of emissions during the operation of the vehicle, but also the provision of clean energy to the vehicle. In this approach, charging the battery of an electric vehicle from renewable sources—a photovoltaic (PV) farm—and its operation can be considered as a totally zero-emission form of transport. The article presents a PV system containing two micro-installations with a capacity of up to 40 kWp each to supply electricity to two parts of the Lublin Science and Technology Park (LSTP) building. Thanks to the innovative monitoring system, it was possible to analyze the consumption and production as well as the effective use of electricity. Statistical analyses of consumption (charging the electric vehicle battery) and electricity production by the PV installation were carried out. It was found that charging an electric vehicle could be a good way to use the surplus energy production from the farm and thus a faster repayment on investment in the PV farm installation.

The article describes the implementation of road driving tests with a vehicle in urban and extra-urban traffic conditions. Descriptions of the hardware and software needed for archiving the data obtained from the vehicle’s on-board diagnostic connector are presented. Then, the routes are analyzed using artificial intelligence methods. In this article, the reference of the route was defined as the trajectory of the driving process, represented by the engine rotational speed, the driving speed, and acceleration in the state space. The state space was separated into classes based on the results of the cluster analysis. In the experiment, five classes were clustered. The K-Means clustering algorithm was employed to determine the clusters in the variant without prior labelling of the classes using the teaching method and without participation of a teacher. In this way, the trajectories of the driving process in the five-state state space were determined. The article compares the signatures of routes created in urban and extra-urban driving conditions. Significant differences between the obtained results were indicated. Interesting methods of displaying the saved data are presented and the potential practical applications of the proposed method are indicated.

Renewable Energy Sources (RESs) are characterized by high unevenness, cyclicality, and seasonality of energy production. Due to the trends in the production of electricity itself and the utilization of hydrogen, distributed generation systems are preferred. They can be connected to the energy distribution network or operate without its participation (off-grid). However, in both cases, such distributed energy sources should be balanced in terms of power generation. According to the authors, it is worth combining different RESs to ensure the stability of energy production from such a mix. Within the mix, the sources can complement and replace each other. According to the authors, an effective system for generating energy from RESs should contain at least two different sources and energy storage. The purpose of the analyses and calculations performed is to determine the characteristics of energy generation from a photovoltaic system and a wind turbine with a specific power and geographical location in the Lublin region in Poland. Another important goal is to determine the substitutability of the sources studied. Probabilistic analysis will be used to determine the share of given energy sources in the energy mix and will allow us to estimate the size of the stationary energy storage. The objective of these procedures is to strive for the highest possible share of renewable energy in the total energy required to charge electric vehicle fleets and to produce low-emission hydrogen for transportation. The article proves that the appropriately selected components of the photovoltaic and wind energy mix located in the right place lead to the self-balancing of the local energy network using a small energy storage. The conclusions drawn from the conducted research can be used by RES developers who intend to invest in new sources of power generation to produce low-emission hydrogen. This is in line with the current policy of the European Union aimed at climate and energy transformation of many companies using green hydrogen.