This article represents a further step in the continuously developing process of improving the prediction capabilities using databases. Its aim is to compare and evaluate the operation, performance and validity of knowledge extraction techniques related to prediction. The innovative part of this study concerns the selection, enrichment and processing of the database used. In particular, the database contains consumption data for an entire city over the course of a year. These data were then enriched with elements concerning the determination of the time and the environmental conditions, in order to take into consideration the correlation of the data with these parameters. Subsequently, after being converted into an editable format, they were processed using techniques such as normalization and factor analysis, which finally led to the prediction process. At this stage, different methods, such as decision trees, deep learning and generalized linear models, were applied and thoroughly analyzed, and both their operation and their effectiveness were compared and evaluated. The present effort, therefore, intends to provide a useful tool that will contribute to future efforts to improve predictions from existing data.

The quality of RF signal coverage of mobile networks, and for example, the parameters of LTE signals at many places, is not reliable enough for intensive data transfer. This fact causes mobile service customers to look for and to apply additional local devices or systems for amplification of the initially supplied RF signal level to improve the quality of the service. This local improvement covers a small area around the customer’s residence. This paper shares some useful results of design, analyses, and optimization for effective performance of the tranceiving antenna for LTE (4G) signals.

The global imperative to transition to renewable energy sources has made photovoltaic technology a key element in the implementation of sustainable energy strategies. The photovoltaic systems are solidly established because they offer a number of advantages, but their performance is strictly linked to the environmental conditions. In particular, coastal installations are confronted with a number of unfavourable conditions, such as humidity or saltiness of the air. This specific category is targeted by the present work. This paper is devoted to the effect that oceanographic conditions are likely to have on the performance of the specific systems. To investigate this purpose, annual data including both oceanographic and photovoltaic performance data are processed and analysed. Both statistical methods such as Factor Analysis and unsupervised learning processes such as k-means cluster analysis, are applied in order to draw correlations. Finally, the conclusions derived from the deployment of these procedures are presented in detail. The innovative part of this work lies not only in the implementation of the specific methodologies and the unsupervised learning approach, but mainly in the combination of the specific databases, which aims to bridge the gap that exists in the research on these specific issues.

This paper presents an exploration of the electromagnetic field characteristics and parameters in the area of coverage of a particular base station, as well as the radio signal strength and the data speed values for optimal service. Although there are many investigations concerning the influence of the electromagnetic field on the reactions of the people positioned in such areas and general impact on humans’ health, it is worth exploring some specific aspects of that problem. One of them is to focus the investigation on some particular radio frequency ranges. The second is to propose some practical consequences of the steps and ways to perform the measurements, with fast opportunity to collect the results and to compare them with identical measurements performed by different equipment or different algorithms for measurements. As a consequence, approaches should be developed to allow relatively accurate measurements of the electromagnetic background with devices more accessible to ordinary people instead of the expensive specialized measuring instruments used by specialists in this field. Such alternative methods of control of the electromagnetic flux radiation could help reliably to update the permissible parameters set in the existing regulations.

In the contemporary era, the pursuit of precise predictions based on datasets is predominantly motivated by the science of computer systems. Therefore, this study attempts to further enhance prediction efforts. A city-wide electricity consumption dataset, which includes temporal and environmental characteristics, is analyzed. This dataset is subjected to rigorous preprocessing to extract relevant characteristics. A wide range of machine learning models, such as XGBoost and Python’s Scikit, are used to build prediction models. These models are then rigorously trained and tuned using sophisticated optimization techniques for optimal performance. Finally, the evaluation of their efficiency, interpretability, and computational efficiency is derived. Furthermore, programming techniques are investigated, and new structures and learning methods elucidate intricate patterns and relationships in the data. This approach facilitates a more comprehensive understanding of the pivotal factors influencing electrical consumption trends and enables the identification of the most suitable methodologies for specific prediction tasks. In conclusion, the present study contributes by utilizing the knowledge gained from previous research for the advancement of predictive analysis. The findings have the potential to be useful in decision-making processes, optimizing resource allocation, and improving urban planning and management practices.