Abstract :

Accurate long-term electricity load forecasting is essential for strategic power system planning but remains challenging in developing regions like Khost, Afghanistan, due to frequent load shedding and data limitations. This study develops a comprehensive forecasting framework using a Multivariate Long Short-Term Memory (LSTM) model. To mitigate data distortions caused by supply constraints, a Standard Load Profile (SLP) was constructed to represent the underlying electricity demand accurately. The model integrates historical consumption with five key explanatory variables: population growth, Gross Domestic Product GDP development, ambient temperature, humidity, and public holidays. Correlation analysis identified temperature as the primary driver of demand, followed by economic growth. The optimized LSTM architecture, featuring two layers (128 and 64 neurons), achieved a Mean Absolute Percentage Error (MAPE) of 3.67%, demonstrating high predictive accuracy. Furthermore, demand was forecasted for the period 2026–2030 under Base, High-Growth, and Low-Growth scenarios. Monte Carlo simulations were employed to generate probabilistic forecasts (P50, P90, and P95), providing a robust quantification of future uncertainty. The findings indicate a consistent rise in future demand, offering a reliable scientific basis for sustainable energy policy and risk-informed infrastructure investment in Khost Province.

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Keywords :

Long-Term Electricity Load Forecasting, Monte Carlo Simulation, Multivariate LSTM, Probabilistic forecasting, Scenario-Based Forecasting, Standard Load Profile

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References :

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