Forecasting Energy Consumption using Recurrent Neural Networks: A Comparative Analysis

TL;DR

This paper demonstrates that LSTM-based recurrent neural networks significantly improve short-term energy consumption forecasting accuracy over traditional models by effectively capturing complex dependencies and external factors.

Contribution

The study introduces an LSTM-based forecasting approach that integrates multiple external variables, outperforming conventional neural networks in energy demand prediction.

Findings

LSTM models achieve lower MAE and RMSE than baseline models.

Incorporating external variables improves forecast accuracy.

Deep learning models are effective for real-world energy forecasting.

Abstract

Accurate short-term energy consumption forecasting is essential for efficient power grid management, resource allocation, and market stability. Traditional time-series models often fail to capture the complex, non-linear dependencies and external factors affecting energy demand. In this study, we propose a forecasting approach based on Recurrent Neural Networks (RNNs) and their advanced variant, Long Short-Term Memory (LSTM) networks. Our methodology integrates historical energy consumption data with external variables, including temperature, humidity, and time-based features. The LSTM model is trained and evaluated on a publicly available dataset, and its performance is compared against a conventional feed-forward neural network baseline. Experimental results show that the LSTM model substantially outperforms the baseline, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). These findings demonstrate the effectiveness of deep learning models in providing reliable and precise short-term energy forecasts for real-world applications.