Electricity Consumption In Pakistan Research Articles

MATHEMATICAL APPROACH TO FORECAST THE ELECTRICITY CRISES IN PAKISTAN AND ITS MITIGATION

Electricity is widely recognized as the backbone of economic success and progress, playing a vital role in driving socioeconomic development. This comprehensive study conducted an in-depth analysis of Pakistan's electricity consumption and aimed to develop a suitable model for forecasting the country's future electricity consumption (E.C.). Due to the strong positive correlation between E.C. of different sectors with Population and Gross Domestic Product (GDP), multiple linear regressions were employed for the analysis of electricity consumption. To predict population and GDP different mathematical model such as linear, Exponential, Polynomial and logarithmic models were applied on population and GDP data. In order to get the best fitted model a number of goodness-of-fit tests Coefficient of Determination, Sum of Square Errors, Sums of Square Regression, Mean Square Error, Root Mean Square Error, And F- Statistics (Adj-R2, SSE, SSR, MSE, RMSE and F Statistics) applied and got the polynomial model to be the most effective one for population and GDP. According to the goodness-of-fit test-based models, Pakistan's population is on track for steady growth, with projections reaching approximately 244428310 in 2025 and further increasing to 319825879 in 2040. This indicates significant demographic changes on the horizon. At the same time, Pakistan's GDP per capita is expected to rise, starting at 1958.3 $ in 2025 and potentially reaching 3,210.9 $ in 2040, signifying a positive trajectory towards economic development and prosperity in the country. Based on the selected models suggested by goodness of fit tests, the study throws light on the expected future electricity consumption in Pakistan. According to these projections, the expected electricity consumption of Pakistan may reach 121,121.1 GWh in 2025, 138,861.8 GWh in 2030, 157,716.8 GWh in 2035, and 177,686.3 GWh in 2040 respectively.

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.

Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan

In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.

Investigating the Interactive Role of Demand Side Factors Potentially Responsible for Energy Crisis in Pakistan

This paper attempts to investigate the dynamic relationship among Energy Consumption (E), Financial System Development (F), Industrailization (I), Agriculture Development (A) and Economic Growth (Y) in case of Pakistan for the period 1971-2018 by using cointegration approach. After confirming the level of stationarity, the presence of long run relationship among the series was tested through newly developed combined cointegration approach in addition to ARDL bound testing with structural break dummy. The short run and long run parameter coefficients were estimated by unrestricted error correction model (UECM) because all the series are found stationary at 1st difference I(1) and sufficient evidence of cointegration. Finally, the direction of causality among the considered variables was achieved through Granger causality test within the framework of VECM. The long run parameter coefficient estimates by UECM indicate that financial development, industrialization, economic growth and decrease in agricultural contribution to GDP induce electricity consumption in Pakistan. We also found that a long-run unidirectional causality is running from the economic growth to electricity consumption which favors the electricity conservation hypothesis in case of Pakistan. The causality running from the electricity consumption to agriculture output coupled with negative parameter coefficient value suggests that electric power deficit is responsible for hampering the agricultural growth in Pakistan. The study suggests that electricity conservation policy in addition to prudent rationing of electric power among the various sectors may greatly contribute to minimize the adverse effects of energy crisis in Pakistan.

Unlocking Household Electricity Consumption in Pakistan

In Pakistan, data for household electricity consumption are available in the form of monthly electricity bills only, and, therefore, are not helpful in establishing appliance-wise consumption. Further, it does not help in establishing the relationship among the household electricity consumption and various driving factors. This study aimed to unlock the household electricity consumption in Pakistan by analyzing electricity bills and investigating the impact of various socioeconomic, demographic, and dwelling parameters and usage of different appliances. The methodology adopted in this study was survey-based data collection of the residential sector. For this purpose, data were collected from 523 dwellings through surveys and interviews in Mirpur city. The results of the data analysis revealed that the average household electricity consumption is 2469 kWh/year with an average family size of seven and an average floor area of 78.91 m2. Based on possession of various appliances, the households were categorized into four types and their consumption patterns were established and compared. Air Conditioned (AC) houses consume 44% more electricity compared to the non-AC houses, whereas an Uninterrupted Power Supply (UPS) consumes electricity equivalent to an AC. The research findings are useful for policy makers and building designers and are discussed in the conclusion section.

THE DETERMINANTS AND FORECASTING OF ELECTRICITY CONSUMPTION IN PAKISTAN

The current study examined the determinants of electricity consumption and also intends to forecast the electricity consumption in Pakistan. The study has used time series data analysis, applied Johansen Cointegration Test, error correction mechanisms and regression for examining determinants and autoregressive integrated moving average model is used for forecasting. The study has used times series secondary annual data on different variables for the period ranging from 1970 to 2018. The results of the study showed that gross domestic product and population have positive impact on electricity consumption. Whereas, National output (GDP) is statistically significant in the determination of total electricity consumption. The results also indicated that increase in the real economic activities has increases the total electricity consumption. Furthermore, the results of electricity consumption model also suggest that electricity price have negative impact on total electricity consumption. As electricity demand is inelastic to electricity price and income, it indicates that fewer substitutes of electricity are available in the market. Hence electricity is essential component of energy for economy. The results of the current study can be useful for the policymakers and government regulatory bodies relating to electricity.

Analyzing the role of industrial sector's electricity consumption, prices, and GDP: A modified empirical evidence from Pakistan

Electricity usage plays a vital role in raising the massive growth in the economy; also, the industrial sector is the key factor of overall energy demand closely related to the economy. The study aims to contribute in two ways. First, the Vector Error Correction Model (VECM) estimates electricity consumption in Pakistan during 1970-2018 to find the relationship between electricity consumption, price, and real gross domestic product. Second, decomposing the overall impact of an unexpected shock on each variableos Dynamic Variance Decomposition Technique applied. The empirical analysis shows that the factors are co-integrated. The results also indicate the long-run relationship between electricity consumption, price, and real gross domestic product in the industrial sector. Further, the VECM analysis responses are also confirmed by the variance decomposition method. The findings confirm the potential of the industrial sector. We propose that formalized and proper assurance of electricity needs and demands at a reasonable price can boost the local industry's confidence and attract foreign investors. However, a strong governance structure should be extended to the public sector to ensure policies that priorities the distribution of energy to businesses for development.

Analysis of electricity consumption in Pakistan using index decomposition and decoupling approach

Electricity consumption plays a significant role in increasing the share of total energy consumption worldwide and has a close association with the economy. This research analyzes the factors in two ways. First, this study estimates the electricity consumption in Pakistan during 1989–2018 using Index Decomposition Analysis between share of electricity consumption, economic development, energy intensity, per capita economy, and population. Second, an appropriate decoupling analysis method is taken to evaluate the relationship between related variables and the process of sustainable growth. These analyses are based on both sectorial and national levels. The outcomes show that the economic structure sector effect was the major driving factor growing total electricity consumption in Pakistan, and energy intensity occurred with positive fluctuations during 1989–2018. The agriculture, industrial, and domestic sectors were the top three sectors that increased electricity consumption. The effects of electricity variations caused by GDP growth and showed seven decoupling states during the whole period. Finally, in recent years, the agriculture sector has a weak decoupling, while energy-intensive sectors had an expensive decoupling and transferred to a weak decoupling over the future period. Consequently, electricity production, energy policies, and energy management need to be better integrated with economic planning exercises.

A forecasting model approach of sustainable electricity management by developing adaptive neuro-fuzzy inference system.

With an exponential industrial growth, an accurate demand forecasting of energy is of prime importance for strategic decision-making and new power policies regarding generation and distribution in the power sector. This is a great impediment in economic development as well as shattering people's daily life. Hence, forecasting of energy demand in emerging markets is one of the most important policy tool used by decision-makers all over the world. This study focused on the forecasting approach of electricity consumption in Pakistan by developing a model that is called ANFIS (Adaptive neuro-fuzzy inference system). A framework was developed comprising economic and demographic variables as input. Previous historical data of GDP, population, industry efficiency, and weather (annual average temperature) was collected as input to the model and electricity consumption as output of the model. By developing ANFIS model, forecasting was done up to 2045. The increasing trends with respect to predictors showed significant association with electricity consumption. The overall least error proved this model best for forecasting and planning electricity demand to achieve sustainability in the power sector.

Dilemma of direct rebound effect and climate change on residential electricity consumption in Pakistan

Energy efficiency improvements owing to technological progress in the energy-using appliances and equipment lower effective price of energy services and, in turn, result into behavioural ex-post increase in the consumption of energy. Thus, on net basis technological progress negatively influences the effectiveness of energy efficiency and circumvent the effects of environmental sustainable policies. This study is the first of its nature in Pakistan that estimates the magnitude of direct rebound effect in residential electricity consumption. Using the time series data from 1973 to 2016, we apply co-integration econometric technique and error correction model to analyse the direct rebound effect. The results indicate that the magnitude of direct rebound effect is 69.5 percent in the long run, while 42.9 percent in the short run. Further, impact of climate change on electricity consumption is examined. The results indicate that consumption of energy is increasing in both short run and long run under climatic changes. These findings suggest that the Government of Pakistan needs to consider rebound effects along with climatic changes in formalizing its energy policies.

Forecasting electricity consumption in Pakistan: the way forward

Growing shortfall of electricity in Pakistan affects almost all sectors of its economy. For proper policy formulation, it is imperative to have reliable forecasts of electricity consumption. This paper applies Holt-Winter and Autoregressive Integrated Moving Average (ARIMA) models on time series secondary data from 1980 to 2011 to forecast total and component wise electricity consumption in Pakistan. Results reveal that Holt-Winter is the appropriate model for forecasting electricity consumption in Pakistan. It also suggests that electricity consumption would continue to increase throughout the projected period and widen the consumption-production gap in case of failure to respond the issue appropriately. It further reveals that demand would be highest in the household sector as compared to all other sectors and the increase in the energy generation would be less than the increase in total electricity consumption throughout the projected period. The study discuss various options to reduce the demand-supply gap and provide reliable electricity to different sectors of the economy.

Functional Time series (FTS) Forecasting of Electricity Consumption in Pakistan

In this study, the monthly electricity consumption for the period of January 1990 through December 2011 in Pakistan is analysed using functional time series (FTS) technique. Electricity consumption model reveals a significant trend due to socio-economic factors. The monthly behavior of forecast values reveals that the electricity consumption is more for summer season and this demand will be increased in future. Forecast model and the forecast values show that the electricity consumption is increasing with the passage of time. The growing energy consumption in the country may be due to economic growth, urbanization process in the region, population growth and industrialization.

Decomposing Change in Energy Consumption of the Agricultural Sector in Pakistan

The agricultural sector provides the majority of employment in Pakistan, especially among the poorest segments of the population. Energy provision affects production and livelihoods, especially with the advance of mechanization in agriculture. Changes in gas and electricity prices affect the income of farmers significantly, thereby reducing the agricultural growth rate over time. Agriculture also has a positive impact on industrial sector exports. The dependence of agriculture on electricity consumption in Pakistan has increased over time, while power generation has not kept up with demand. This article decomposes energy consumption in Pakistan and analyzes the behaviour of change in agricultural production, energy intensity and structural changes over the time.

Determinants of electricity consumption function in Pakistan: Old wine in a new bottle

The objective of the study is to re-investigate the multivariate electricity consumption function for Pakistan, particularly, economic growth, foreign direct investment and population growth over a 36-year time period, i.e., between 1975 and 2010. The study employed the bounds-testing procedure for cointegration which examines the short-run and long-run estimates. Dynamic short-run causality test is applied to determine the causality direction between electricity consumption and its determinants, by using Wald-F statistics. The results reveal that determinants of electricity consumption function are cointegrated and influx of foreign direct investment, income and population growth is positively related to electricity consumption in Pakistan. However, the intensity of these determinants is different on electricity consumption. If there is 1% increase in income, foreign direct investment and population growth; electricity consumption increases by 0.973%; 0.056% and 1.605%, respectively. This infers that income, foreign direct investment and population growth induce an increase in electricity consumption in Pakistan. Dynamic short-run causality test indicates that there has been unidirectional causality which is running from population growth to electricity consumption in Pakistan.