Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another disadvantage is the presence of false information in the analyzed training data. False information is the result of errors in measuring, collecting, and transferring data. Usually, trial error techniques are used to determine the number of hidden nodes. To compare prediction accuracy, several models have been proposed, including a conventional ANN and a wavelet ANN. In this research, the influence of different learning algorithms was also examined. The main differences were the training time and number of epochs. To improve the quality of the raw data and remove false information, the research uses the technology of normalizing raw data. The basis of normalization was the technology of the Z-score of the data and determination of the energy‒entropy ratio. The purpose of this research was to compare the accuracy of various data processing and neural network training algorithms suitable for use in data-driven (black box) modeling. For this research, we used a software application created in the MATLAB environment. The app uses wavelet transforms to compare different heat demand prediction methods. The use of several wavelet transforms for various wavelet functions in the research allowed us to determine the best algorithm and method for predicting heat production. The results of the research show the need to normalize the raw data using wavelet transforms. The sequence of steps involves following milestones: normalization of initial data, wavelet analysis employing quantitative criteria (energy, entropy, and energy‒entropy ratio), optimization of ANN training with information energy–entropy ratio, ANN training with different training algorithms, and evaluation of obtained outputs using statistical methods. The developed application can serve as a control tool for dispatchers during planning.

The core of this publication is the design of a system for evaluating the condition of production equipment and machines by monitoring selected parameters of the production process with an additional sensor subsystem. The main positive of the design is the processing of data from the sensor layer using artificial intelligence (AI) and expert systems (ESs) with the use of edge computing (EC). Sensor information is processed directly at the sensor level on the monitored equipment, and the results of the individual subsystems are stored in the form of triggers in a database for use in the predictive maintenance process. The whole solution includes the design of suitable sensors and of the implementation of the sensor layer, the description of data processing algorithms, the design on the communication infrastructure for the whole system, and tests in the form of experimental operation of the device in laboratory conditions. The solution includes the visualisation of the production system status for the operator using an interactive online map.

The core of the publication is the design of a system for evaluating the condition of production equipment and machines by monitoring selected parameters of the production process with an additional sensor subsystem. The main positive of the design is the processing of data from the sensor layer using Artificial Intelligence (AI) and Expert Systems (ES) with the use of Edge Computing (EC). Sensor information is processed directly at the sensor level on the monitored equipment, and the results of the individual subsystems are stored in the form of triggers in a database for use in the predictive maintenance process. The whole solution includes the design of suitable sensors and the design of the implementation of the sensor layer, the description of data processing algorithms, the design on the communication infrastructure for the whole system, and tests in the form of experimental operation on the device in laboratory conditions. The solution includes visualization of the production system status for the operator using an interactive online map.

The aim of this contribution is to propose the architecture for a layered design of the production system. This proposal uses the IEC 62443 norm, including the Defense-in-Depth strategy and proven technical principles applicable in a Smart Factory with a focus on communication security. Firstly, the identification of communication forms and trends in the Smart Factory environment was identified considering the spectrum of communication protocols used within various types of automation structures used in modern production facilities. The next part of the work deals with the definition of wired and wireless forms of data transfers in production systems including their advantages and disadvantages from the view of cybernetic safety and threads in communication systems, together with the description of norms from the field of security of communication systems applicable in the industrial environment. The core of this work is the proposal of the methodology to secure the Smart Factory production system in the Industry 4.0 environment. The proposal defines important implementation steps together with a summarization of the generally applicable basic principles suitable for the process of securing a Cyber production system or Smart Factory in an industrial environment, including the example of an Iptables firewall configuration within the OPC UA communication protocol and the real example of a Smart Factory production system segmentation.

This article first describes the current state of the problem in the area of communication in modern factories. Next in the article is given a summary of the requirements that must be implemented for the possibility of establishing a compatible and safe communication system. In the final part of the article is given a proposal of communication model suitable for the implementation.