Uncertainty-aware energy forecasting and environmental impact simulation using Monte Carlo and deep learning

This paper presents a simulation system utilizing Long Short-Term Memory (LSTM) forecasting models combined with Monte Carlo methods to predict energy generation and model uncertainty from renewable and fossil fuel sources in the Adrar region of Algeria. The study focuses on short-term load forecasting for the Kabertane wind field, the Adrar solar photovoltaic farm, and overall electrical demand. Separate LSTM models predict each renewable source’s output, which are aggregated and subtracted from load demand forecasts to determine necessary fossil fuel requirements. Monte Carlo methods quantify uncertainty by fitting error distributions to forecast residuals and generating multiple realizations with added noise. This probabilistic approach provides robust assessment of economic costs and environmental impacts of energy production. Results demonstrate significant cost savings and CO2 emission reductions through renewable energy incorporation while emphasizing the critical role of uncertainty modeling in optimizing energy production, cost, and environmental sustainability in the region.