Univariate vs multivariate predictability analysis of electricity consumption by LSTM approach: An empirical evaluation

Precise prediction of EC is crucial for planning, managing, and cost-effective operation of power grids, as it is a time series problem. In recent years, numerous studies have analyzed the behavior and quality of time series forecasts in different areas, including the EC. New models or variants of traditional algorithms have also been proposed, usually taking advantage of the increasing amount of data available and of today's computing power, such as LSTMs. One of the most important characteristics in the selection of a forecasting method is the number of variables that must be taken into account in the prediction of the time series since most of these variables are subject to external influences. EC is dependent on a number of external factors, such as climatic factors, economic factors, and the spot price of electricity. The EC dataset may be either univariate or multivariate. When dealing with univariate time series data, it's important to utilize specific methods that take only the historical values of the variable into consideration for accurately estimating its pattern. Prediction methods that analyze dependencies and correlations between variables to predict future values are also suitable for multivariate time series. Nevertheless, these approaches usually need more time to compute and train, and they might not even be the most appropriate way to go, because the increased complexity of the model used could outweigh any possible improvement in prediction accuracy. In this paper, a thorough comparison study was conducted to analyze the effectiveness of univariate and multivariate predictive analysis on two separate sets of EC data at hourly and daily intervals. To accomplish this, the LSTM algorithm was utilized, which has recently been widely used and recognized as the best-performing algorithm in EC forecasting studies. The comparative analysis is complemented by a comprehensive literature review, meticulously presented in a tabular format, to offer a comprehensive understanding of the univariate and multivariate forecasting methodologies and their respective outcomes. This study stands out due to its incorporation of an extensive literature review to support the experimental research, ensuring a thorough evaluation. Based on experimental studies, univariate forecasting analysis outperformed multivariate forecasting analysis for both hourly and daily interval data sets. Furthermore, the R-squared results of the univariate and multivariate predictive analyses conducted with the hourly data set are significantly higher than those of the same predictive analyses in the daily interval.