Data-driven Insights for Informed Decision-Making: Applying LSTM Networks for Robust Electricity Forecasting in Libya

TL;DR

This paper demonstrates that LSTM neural networks, enhanced with exogenous factors, provide highly accurate forecasts of electricity load, generation, and deficits in Libya, aiding better energy management in volatile, data-scarce environments.

Contribution

The study introduces an optimized LSTM framework incorporating external variables for improved electricity forecasting in Libya, outperforming traditional models.

Findings

LSTM achieved the lowest error metrics among tested models.

Inclusion of temperature and humidity improved forecast accuracy.

LSTM effectively modeled non-stationary and seasonal patterns.

Abstract

Accurate electricity forecasting is crucial for grid stability and energy planning, especially in Benghazi, Libya, where frequent load shedding, generation deficits, and infrastructure limitations persist. This study proposes a data-driven approach to forecast electricity load, generation, and deficits for 2025 using historical data from 2019 (a year marked by instability) and 2023 (a more stable year). Multiple time series models were applied, including ARIMA, seasonal ARIMA, dynamic regression ARIMA, exponential smoothing, extreme gradient boosting, and Long Short-Term Memory (LSTM) neural networks. The dataset was enhanced through missing value imputation, outlier smoothing, and log transformation. Performance was assessed using mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error. LSTM outperformed all other models, showing strong capabilities in modeling non-stationary and seasonal patterns. A key contribution of this work is an optimized LSTM framework that integrates exogenous factors such as temperature and humidity, offering robust performance in forecasting multiple electricity indicators. These results provide practical insights for policymakers and grid operators to enable proactive load management and resource planning in data-scarce, volatile regions.