Advancements in Machine Learning Techniques for Multivariate Time Series Forecasting in Electricity Demand

Abstract

The exact prediction of electrical energy usage stands as a vital operational tool for power system management while the evolving market landscape with rising data complexity continues to exist. The accurate prediction of electricity demand stands vital to produce optimized power generation and keep the electrical grid stable and support efficient use of renewable energy. MTS forecasting techniques for electricity consumption analysis take multiple variables which integrate weather elements and economic indicators with social nuances and environmental aspects. This study explores traditional ARIMA and VAR statistical models together with contemporary machine learning methods that include SVM and RF and GBM along with RNN and LSTM networks and their combination algorithms. This paper evaluates various techniques to identify major barriers in electricity consumption prediction particularly related to managing multidimensional non-linear and noisy data sets. The implementation of multiple variables leads to improved accuracy in forecasts since it surpasses what univariate models can achieve. The review delves into sophisticated forecasting approaches which merge statistical and machine learning approaches and deep learning methods along with discussions about crucial data preprocessing operations like normalization, missing value handling and feature development. The paper ends by discussing upcoming electricity consumption forecasting patterns including real-time data analysis together with explainable artificial intelligence technology and flexible predictive models for advanced energy system requirements. Further research is required to handle present-day limitations which prevent the use of models that combine high accuracy with scalability and real-time processing needs.