Comparative Analysis of Methods for Prediction Continuous Numerical Features on Big Datasets

The object of research is the process of choosing a method for predicting continuous numerical features on big datasets. The importance of the study is due to the fact that today in various subject areas it is necessary to solve the problem of predicting performance indicators based on data collected from different sources and presented in different formats, which is the task of big data analysis. To solve the problem, the methods of statistical analysis were considered, namely multiple linear regression, decision trees and a random forest. An array of extensive data was built without specifying the subject area, its preliminary processing, analysis was carried out to establish the correlation between the features. The processing of the big data array was carried out using the technology of parallel computing by means of the Dask library of the Python language. Since working with big data requires significant computing resources, this approach does not require the use of powerful computer technology. Prediction models were built using multiple linear regression methods, decision trees and a random forest, visualization of the prediction results and analysis of the reliability of the constructed models. Based on the results of calculating the prediction error, it was found that the greatest prediction accuracy among the considered methods is the random forest method. When applying this method, the prediction accuracy for a dataset of numerical features was approximately 97 %, which indicates a high reliability of the constructed model. Thus, it is possible to conclude that the random forest method is suitable for solving prediction problems using large data sets, it can be used for datasets with a large number of features and is not sensitive to data scaling. The developed software application in Python can be used to predict numerical features from different subject areas, the prediction results are imported into a text file

Aggregation of Multidimensional Data for the Decision Support Process for the Management of Microgrids with Renewable Energy Sources

The object of research is the process of processing and storing data when making decisions on managing the life cycle of electricity generation and consumption in microgrids with renewable energy sources. The prospects of the study are due to the fact that in order to provide a full-fledged decision support process in the management of microgrids with renewable energy sources, it is necessary to consolidate and manipulate multidimensional data in multithreading and online information processing. To solve the problem, the theoretical methods of analysis, abstraction, induction and deduction were used. To ensure multidimensionality and multithreading of data processing, it is proposed to develop a data warehouse based on the snowflake data model. Efficiency of information processing in real time is provided by an operational database built on the principle of OLTP. The organization of the joint work of the data warehouse with the operational database, the consolidation and manipulation of data is provided by triggers. The result of the work is a data warehouse that will be used in the decision support system for managing energy microgrids, which will improve the efficiency of data processing and storage. This is achieved by combining the work of a centralized data warehouse with an operational database, as well as the use of a separate data mart for each user of the system. The practical significance of the work lies in the fact that the data warehouse will become part of the decision support system for processing information about the life cycle of energy in the management of energy infrastructure. Compared to using a single database for a decision support system, this approach ensures the speed of working with data and allows differentiating between the use of a data warehouse for analytics and data manipulation operations. The data warehouse was deployed in a cloud environment on the Amazon Web Services (AWS) platform and the Amazon Relational Database Service (Amazon RDS) web service. Secure access to client data is implemented using data marts

Development of Expert Assessment Methods in Planning Energy Supply of Buildings with Renewable Energy Sources

The object of research is the process of expert evaluation in planning the energy supply of buildings using renewable energy sources. The conducted research is based on the application of system analysis methods to formalize the process of expert evaluation in the planning of an energy system with renewable energy sources. Here were used methods of expert evaluation of characteristics of qualitative criteria, methods of the theory of fuzzy sets and fuzzy logic for the formation of the value of criteria, and methods of estimating the reliability of the expert evaluation. Methods of structural analysis and functional modelling of information systems are used to build structural and functional models of the expert evaluation process. The issue of creating an appropriate information system for planning a power system with renewable energy sources is considered. One of the components of the information system is the unit for evaluating candidate experts. A six-level algorithm of the hierarchical structure of expert selection is proposed. As a result of the algorithm, an expert group is formed. This paper shows the process of narrowing the circle of experts from twenty to three candidates. The list of criteria influencing the choice of experts is formed: length of service, availability of the certificate, the efficiency of decision-making, education. The process of assessing the stability of experts' opinions is shown. It is proposed to use the method of processing the opinions of experts to find the value of the membership functions of qualitative criteria. As a result of the study, a group of three experts was formed, whose opinions are taken into account when choosing alternatives to the energy system. In accordance with the proposed information technology of energy supply planning of buildings using renewable energy sources, an information system in the form of a web-oriented application is proposed. A separate part of the information system is a subsystem for working with experts. The diagram of sequence of actions of the expert and the interface of work with system is developed. The use of the information system allowed to increase the efficiency of questionnaires of experts and decision-making on the choice of the optimal structure of the power system as a whole