Impact of Climate Change on Electricity Demand: A Case Study of      Pakistan

Mahmood, Rafat; Saleemi, Sundus; Amin, Sajid

doi:10.30541/v55i1pp.29-47

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…De Cian et al [24] used data from 31 countries for the period 1978-2000, the result suggested that with higher temperatures there will be higher consumption during summer in warmer countries. Mahmood et al [25] used Pakistan's monthly temperature data to explore the relationship between electricity demand and temperature. The results showed that demand for electricity will rise sharply in the summer due to climate change.…”

Section: Introductionmentioning

confidence: 99%

Hot Weather and Electricity Consumption by Migrant Workers in China

Jihong¹,

Zhang²,

Yang³

2021

Preprint

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China's urbanization has entered a new phase, and more and more migrant workers have become urban residents, and rapid urbanization is expected to further increase global energy consumption. Meanwhile, climate change will increase the refrigeration demand of residents, which is generally met with electric air conditioners and fans. It is necessary to be concerned about the energy consumption of migrant workers to ensure that society develops sustainably and stably. This paper compares the influence of temperature on the electricity consumption by rural residents, migrant workers and urban residents in Guangzhou in 2017.The results show that the 1°C rise in temperature leads to a 3% increase in electricity consumption by rural households, a 7% increase by rural migrant worker, a 9% increase by urban household. More seriously, the actual price of the migrant workers cost burden of electricity consumer spending is the biggest of the three types of families, make life in the city of migrant workers families facing serious electricity consumption inequality in hot weather.

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Section: Introductionmentioning

confidence: 99%

Hot Weather and Electricity Consumption by Migrant Workers in China

Jihong¹,

Zhang²,

Yang³

2021

Preprint

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“…In short, the consumption of electricity by households and businesses for heating and cooling purposes is primarily driven by weather phenomenon. A change in weather patterns causes a shift in the electricity load demand, which if not foreseen may result into an imbalance between supply and demand [17]. This imbalance may also trigger an increase in production cost of electricity due to the use of expensive production units (kerosene oil based units for example) and wastage of unutilized produced electricity due to overproduction.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters

et al. 2020

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Electricity, a fundamental commodity, must be generated as per required utilization which cannot be stored at large scales. The production cost heavily depends upon the source such as hydroelectric power plants, petroleum products, nuclear and wind energy. Besides overproduction and underproduction, electricity demand is driven by metrological parameters, economic and industrial activities. Therefore, the region specific accurate electric load forecasting can help to effectively manage, plan, and schedule appropriate low cost electricity generation units to decrease per unit cost and provision of on time energy for maximum financial benefits. Machine learning (ML) offers different supervised learning algorithms including multiple linear regression, support vector regressors with different kernels, k-nearest neighbors, Random Forest and AdaBoost to forecast the time series data, but the performance of these algorithms is data dependent. It is vitally important to consider correlated metrological parameters of the specific region for accurate prediction of electricity load demand using ML based forecasting models to minimize the price per unit. In this study, an algorithm is proposed to select least cost electric load forecasting model (lcELFM) using correlated meteorological parameters. We developed least cost forecasting models by minimizing root mean squared error, mean absolute error, and mean absolute percentage error. For simulations, the recorded electricity demand data is taken from a substation of water and power development authority Muzaffarabad city from 1 st January 2014 to 31 st December 2015. The meteorological time series data are obtained from the substation of Pakistan meteorological department for the same period and same region. Empirical results demonstrate the robustness of the proposed algorithm to select lcELFM. Moreover, SVR (Radial) based electric load forecasting model proves to be the robust model when built using correlated features (temperature and dew point) for the said region and in turn can save up to PKR 0.313 million daily.INDEX TERMS Electricity load demand, electricity load forecasting, least cost forecasting model, machine learning, meteorological parameters.

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Residential and industrial electricity consumption in Taiwan: Weather or macroeconomic condition (or both)

Liu

2022

Energy Strategy Reviews

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Impact of Climate Change on Electricity Demand: A Case Study of Pakistan

Cited by 6 publications

References 24 publications

Hot Weather and Electricity Consumption by Migrant Workers in China

Hot Weather and Electricity Consumption by Migrant Workers in China

Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters

Residential and industrial electricity consumption in Taiwan: Weather or macroeconomic condition (or both)

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