Research and Development of Models for Intelligent Analysis of Big Data in Planning Tasks

The utilisation of machine learning techniques in the analysis of big data has become a standard practice for forecasting the outcomes of various sectors of the economy. This scientific article analyses changes in electricity consumption volumes in the Republic of Kazakhstan from 2002 to 2022. The study examines the dynamics of growth or reduction in consumption volumes. Data was prepared for analysis and model construction by employing outlier assessment methods. The work considers the Holt-Winters, SARIMA, and LSTM models, comparing their advantages and disadvantages. Accuracy metrics for forecasts and the Dickey-Fuller test for stationarity are also assessed. The application of forecasting models yielded diverse output data for predictive values, and it also examined the quality of training neural networks for analysing big data, specifically electricity consumption volumes. As an alternative, recurrent neural networks (RNNs) were utilised. The study revealed that this category of neural networks achieves results that closely approximate actual data and ensures precise training of neural networks for forecasting tasks. Moreover, the article explores the bidirectional RNN, a variation of recurrent networks that offers superior problem-solving quality in some instances. Recurrent networks process input sequences sequentially, and any alteration in data sequence can completely transform the representation extracted from the sequence. Hence, they excel in tasks where sequence order matters, such as temperature forecasting.