Forecasting electricity consumption in Pakistan: the way forward

Hussain, Anwar; Rahman, Md. Ashib; Memon, Junaid Alam

doi:10.1016/j.enpol.2015.11.028

Cited by 159 publications

(78 citation statements)

References 47 publications

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“…From the results of ARIMA model of this study, it is estimated that by the end of forecasting period, i.e., 2035, electricity share in total fuel mix shall be 17.74% which corresponds to 13.1 million TOE and most of the electricity is projected to be consumed in industrial sector (5.3 million TOE), followed by domestic (5.2 million TOE), agriculture (0.9 million TOE) and commercial (0.8 million TOE) sectors as shown in Figure 6. The electricity demand forecasted by Hussain, Rahman and Memon [19] for Pakistan using ARIMA model from 2015 to 2020 closely match with the results of this study as shown in Table 7. The National It is worth mentioning here that Energy Security Plan (NESP) developed by the government of Pakistan for period 2005-2030 had also envisaged the increased demand for fossil fuels for meeting the future energy needs.…”

Section: Resultssupporting

confidence: 81%

“…The partial correlation measures the correlation between observations that are k time periods apart after controlling for correlations at intermediate lags, i.e., it removes the influence of these intervening variables. In other words, partial auto-correlation is the correlation between Y t and Y t−k after removing the effect of intermediate Y's [19]. Further, before finally selecting a forecasting model, the residuals from the estimation is observed in the previous step and checked whether any of the auto-correlations and partial correlations of the residuals are individually and statistically significant or not.…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…Amongst which the most important one is the occurrence of "unit roots". For instance, as shown below, the autoregressive model of order 1 with lag 1 [19,20]:…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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Abstract:Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan's energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector's demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

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Section: Resultssupporting

confidence: 81%

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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“…However, in quantum computing, the initial solution is generated by using the concept of qubit to assign a real value in the interval (0,1), consistent with Equation (6). A qubit is the smallest unit of information for a quantum representation, and is mathematically represented as a column vector (unit vector), which can be identified in 2D Hilbert space.…”

Section: Tabu Search (Ts) Algorithm and Quantum Tabu Search (Qts) Algmentioning

confidence: 99%

“…In the last few decades, models for improving the accuracy of load forecasting have included the well-known Box-Jenkins' ARIMA model [6], exponential smoothing model [7], Kalman filtering/ linear quadratic estimation model [8][9][10], the Bayesian estimation model [11][12][13], and regression models [14][15][16]. However, most of these models are theoretically based on assumed linear relationships between historical data and exogenous variables and so cannot effectively capture the complex nonlinear characteristics of load series, or easily provide highly accurate load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

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Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

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Forecasting of Turkey's electricity generation and CO₂ emissions in estimating capacity factor

Şahin

2018

Env Prog and Sustain Energy

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In this study, Turkey's electricity generation between 2017 and 2021 is forecasted. To achieve this, past data of capacity factor of thermal, hydro, geothermal, wind, and solar power plants from 2006 to 2016 is correlated by using exponentially damped sinusoidal approximation. Estimated installed capacity values of electricity generation from 2017 to 2021 are taken from Turkish Electricity Transmission Company (TEİAŞ). The capacity factor and electricity generation of each power plants are forecasted from 2017 to 2021 for two different scenarios which are based on TEİAŞ's 5‐year installed capacity projection. According to scenario 1 and 2, Turkey's electricity generation is projected in the range of 332.5–362.9 TWh and 322.0–345.2 TWh, respectively. Additionally, projected CO2 emissions due to electricity generation are estimated in the range of 178.7–199.2 million tons and 171.6–187.1 million tons for the Scenario 1 and Scenario 2, respectively. Also, this study is compared with Turkey's vision 2023 electricity generation targets. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 56–65, 2019

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Forecasting electricity consumption in Pakistan: the way forward

Cited by 159 publications

References 47 publications

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Forecasting of Turkey's electricity generation and CO₂ emissions in estimating capacity factor

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Forecasting electricity consumption in Pakistan: the way forward

Cited by 159 publications

References 47 publications

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Forecasting of Turkey's electricity generation and CO2 emissions in estimating capacity factor

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Product

Resources

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Forecasting of Turkey's electricity generation and CO₂ emissions in estimating capacity factor