Zoya Zahid2025-10-022025-10-022020https://escholar.umt.edu.pk/handle/123456789/7775Over the last decade, the energy sector has experienced a major modernization cycle. Its network is undergoing accelerated upgrades. The instability of production, demand, and markets is far less stable than ever before. Also, the corporate concept is profoundly questioned. Many decision- making processes in this competitive and complex setting depend on probabilistic predictions to measure unpredictable futures. In recent years, the interest in probabilistic energy forecasting analysis has rapidly begun, even though many articles in the energy forecasting literature focus on points or single-valuation forecasting. In Pakistan, the bulk of early studies require various kinds of econometric modeling. However, the simulation of time series appears to deliver stronger results given the projected economic and demographic parameters usually deviate from the achievements. We used machine learning methods, such as ARIMA and Long-Short - Term Memory (LSTM), to calculate Pakistan's future primary energy demand from 2019 to 2030. In this study, we used the methods used in machine learning. We have accessed the dataset of the electricity sector for forecasting purposes from the hydrocarbon development institute of Pakistan (HDIP). The dataset of HDIP is from 1999 to 2019 with different attributes like Electricity Installed Capacity (Hydel Thermal (WAPDA), Thermal (K-Electric), Thermal (IPPs), Nuclear), Energy Consumption by Sector (Domestic, Commercial), Resource Production (Oil, Gas, Coal, Electricity), and Resource Consumption (Oil, Gas, Coal, Electricity). We have forecast the energy demand of each attribute till 2030 with ARIMA technique, and LSTM. Predicting overall primary energy demand using machine learning appears to be more accurate than summing up the individual forecasts. Tests have shown that specific energy sources exceed annual growth levelsenThesis