A Smart Analysis and Visualization of the Power Forecasting in Pakistan

Abstract

Over the last decade, the energy sector has experienced a significant 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 more reliable results, given the projected economic and demographic parameters usually deviate from the achievements. The machine learning technique “ARIMA” and deep learning technique Long Short-Term Memory “LSTM,” are used to calculate Pakistan’s future primary energy demand from 2019 to 2030. In this paper, it is accessed that 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). It is visualized and forecasted the energy demand of each attribute until 2030. Predicting overall primary energy demand using machine learning appears to be more accurate than summing up the individual forecasts.