Load Forecasting in the Era of Smart Grids: Opportunities and Advanced Machine Learning Models

TL;DR

This paper evaluates advanced machine learning models, including gradient boosting and neural networks, for short-term load forecasting in smart grids, demonstrating improved accuracy over classical methods.

Contribution

It introduces a hybrid framework and compares multiple ML models, including LSTM and GRU, for enhanced load forecasting accuracy in smart grid applications.

Findings

ML models outperform ARIMA baseline in accuracy

Gradient boosting methods like XGBoost and LightGBM are effective

Neural networks such as LSTM and GRU show promising results

Abstract

Electric energy is difficult to store, requiring stricter control over its generation, transmission, and distribution. A persistent challenge in power systems is maintaining real-time equilibrium between electricity demand and supply. Oversupply contributes to resource wastage, while undersupply can strain the grid, increase operational costs, and potentially impact service reliability. To maintain grid stability, load forecasting is needed. Accurate load forecasting balances generation and demand by striving to predict future electricity consumption. This thesis examines and evaluates four machine learning frameworks for short term load forecasting, including gradient boosting decision tree methods such as Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). A hybrid framework is also developed. In addition, two recurrent neural network architectures, Long Short Term Memory (LSTM) networks and Gated Recurrent Units (GRU), are designed and implemented. Pearson Correlation Coefficient is applied to assess the relationships between electricity demand and exogenous variables. The experimental results show that, for the specific dataset and forecasting task in this study, machine learning-based models achieved improved forecasting performance compared to a classical ARIMA baseline.