Forecasting Residential Electricity Demand In Saudi Arabia: The Role Of Energy Efficiency In Achieving The 2060 Net-Zero Target

Dr. Faisal A. Al-Rashid; Prof. Eleanor J. Vance

Authors

Dr. Faisal A. Al-Rashid King Abdullah Petroleum Studies and Research Center (KAPSARC), Riyadh, Saudi Arabia
Prof. Eleanor J. Vance Department of Sustainable Energy Engineering, Imperial College London, London, United Kingdom

Keywords:

Residential electricity demand, Energy efficiency, Saudi Arabia, Net-zero

Abstract

Background: Saudi Arabia's residential sector is the largest consumer of electricity in the Kingdom, posing a significant challenge to the nation's ambitious goal of achieving net-zero carbon emissions by 2060. While numerous studies have forecasted near-term demand, a long-term, policy-driven analysis integrating the potential of large-scale energy efficiency programs remains a critical research gap.

Objective: This study aims to forecast residential electricity demand in Saudi Arabia up to 2060 under three distinct energy efficiency scenarios—Business-As-Usual (BAU), Moderate Energy Efficiency (MEE), and Aggressive Energy Efficiency (AEE)—to quantify the potential contribution of energy efficiency to the Kingdom's net-zero target.

Methods: A hybrid forecasting model combining Autoregressive Integrated Moving Average (ARIMA) with key econometric drivers (population, GDP, electricity prices, climate data) was developed. Using historical data from 2000 to 2024, the model projects future demand trajectories for each scenario, reflecting varying levels of policy intervention, technological adoption, and building retrofitting efforts.

Results: The BAU scenario projects a substantial increase in residential electricity demand, creating a significant challenge for decarbonization. The MEE scenario demonstrates notable savings, but falls short of a net-zero trajectory. The AEE scenario, however, reveals that a comprehensive and aggressive push for energy efficiency could lead to a stabilization or even a reduction in residential demand, fundamentally altering the energy landscape and making the 2060 net-zero goal significantly more attainable.

Conclusion: Achieving Saudi Arabia's 2060 net-zero pledge is inextricably linked to managing residential electricity demand. This study provides quantitative evidence that aggressive, sustained investment in energy efficiency is not merely beneficial but essential. The findings underscore the urgent need for policymakers to implement a robust framework of advanced building codes, appliance standards, financial incentives, and public engagement to unlock the full potential of energy efficiency as a cornerstone of the nation's sustainable energy transition.

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References

Abdel-Aal, R.E., Al-Garni, Ahmed Z., 1997. Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy 22 (11), 1059–1069.

Al Harbi, Fahad, Csala, Denes, 2019. Saudi Arabia’s electricity: energy supply and demand future challenges. In: Proceedings of the 2019 First Global Power, Energy and Communication Conference (GPECOM). IEEE, pp. 467–472.

Aldubyan, Mohammad, Belaid, Fateh, Gasim, Anwar, 2023. The rebound effect in residential energy demand: the case of Saudi Arabia. KAPSARC.

Aldubyan, Mohammad, Gasim, Anwar, 2021. Energy price reform in Saudi Arabia: modeling the economic and environmental impacts and understanding the demand response. Energy Policy 148, 111941.

Aldubyan, Mohammad, Moncef Krarti, Eric Williams. Residential Energy Model for Evaluating Energy Demand and Energy Efficiency Programs in Saudi Residential Buildings. 2020.

Al-Homoud, Mohammad S., Krarti, Moncef, 2021. Energy efficiency of residential buildings in the Kingdom of Saudi Arabia: review of status and future roadmap. J. Build. Eng. 36, 102143.

Almulla, Youssef, 2015. Gulf Cooperation Council (GCC) Countries 2040 Energy Scenario Electricity Generation Water Desalination.

Almushaikah, AbdulRahman S., Almasri, R., 2021. Evaluating the potential energy savings of residential buildings and utilizing solar energy in the middle region of Saudi Arabia–case study. Energy Explor. Exploit. 39 (5), 1457–1490.

Al-Tamimi, Nedhal, 2017. A state-of-the-art review of the sustainability and energy efficiency of buildings in Saudi Arabia. Energy Effic. 10 (5), 1129–1141.

Belaid, F., Aldubyan, M., 2021. The role of residential energy efficiency in shaping the energy transition in Saudi Arabia: key challenges and initiatives. In: Proceedings of the IAEE Energy Forum. International Association for Energy Economics: Cleveland, OH, USA, 19–23.

Belaïd, Fateh, 2025. Designing long-lasting inventions for residential energy efficiency. Nat. Rev. Clean. Technol. 1 (1), 22–23.

Box, George E.P., Jenkins Gwilym M. Time series analysis: forecasting and control. 1976.

Dong, Bing, Li, Zhaoxuan, Rahman, S.M. Mahbobur, Vega, Rolando, 2016. A hybrid model approach for forecasting future residential electricity consumption. Energy Build. 117, 341–351.

Fahmy, Marwa Salah E.I.Din, Ahmed, Farhan, Durani, Farah, Bojnec, Stefan, Ghareeb, Mona Mohamed, 2023. Predicting electricity consumption in the Kingdom of Saudi Arabia. Energies 16 (1), 506.

Gao, Feng, Chi, Hong, Shao, Xueyan, 2021. Forecasting residential electricity consumption using a hybrid machine learning model with online search data. Appl. Energy 300, 117393.

Gonzalez Grandona, T., J. Schwenzer, T. Steens, J. Breuing. 2024. Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine.

Hong, Tao, Pinson, Pierre, Fan, Shu, 2014. Global energy forecasting competition 2012. Int. J. Forecast. 30 (2), 357–363.

Hyndman, Rob J., George, A., 2018. Forecasting: principles and practice. OTexts.

Hyndman, Rob J., Khandakar, Yeasmin, 2008. Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27, 1–22.

IEA, 2023. Electricity Information Overview. https://www.iea.org/reports/electricity-information-overview/electricity-consumption.

Jo, Ha-Hyun, Jang, Minwoo, Kim, Jaehyeok, 2020. How population age distribution affects future electricity demand in Korea: applying population polynomial function. Energies 13 (20), 5360.

KAPSARC, 2016. The KAPSARC Energy Model for Saudi Arabia: Documentation of the Model Build Called ‘KEM-SA_v9.16′. https://www.kapsarc.org/wp-content/uploads/2016/11/KEM-SA_documentation_v9.16.pdf.

Krarti, Moncef, Aldubyan, Mohammad, 2021. Role of energy efficiency and distributed renewable energy in designing carbon neutral residential buildings and communities: case study of Saudi Arabia. Energy Build. 250, 111309.

Krarti, Moncef, Aldubyan, Mohammad, 2022. Peak demand-based optimization approach for building retrofits: case study of Saudi residential buildings. Energy Effic. 15 (8), 69.

Krarti, Moncef, Aldubyan, Mohammad, Williams, Eric, 2020. Residential building stock model for evaluating energy retrofit programs in Saudi Arabia. Energy 195, 116980.

Kwiatkowski, Denis, Phillips, Peter C.B., Schmidt, Peter, Shin, Yongcheol, 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J. Econ. 54 (1-3), 159–178.

Liang, Jie, Liang, Yi, 2017. Analysis and modeling for China’s electricity demand forecasting based on a new mathematical hybrid method. Information 8 (1), 33.

Liu, Che, Sun, Bo, Zhang, Chenghui, Li, Fan, 2020. A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine. Appl. Energy 275, 115383.

Matar, Walid, 2016. Beyond the end-consumer: how would improvements in residential energy efficiency affect the power sector in Saudi Arabia? Energy Effic. 9, 771–790.

Matar, Walid, Mansouri, Noura, Umeozor, Evar, 2023. Energy Policy Pathways to Inform Climate Policy in Saudi Arabia.

Mikayilov, J.I, Darandary, A., 2023. Commercial electricity demand modeling: Do regional differences matter for Saudi Arabia? Energy Reports 10, 2826–2836.

Mikayilov, Jeyhun I., Darandary, Abdulelah, Alyamani, Ryan, Hasanov, Fakhri J., Alatawi, Hatem, 2020. Regional heterogeneous drivers of electricity demand in Saudi Arabia: modeling regional residential electricity demand. Energy Policy 146, 111796.

Mikayilov, Jeyhun I., Hasanov, Fakhri J., Olagunju, Waheed, Al-Shehri, Mohammad H., 2020. Electricity demand modeling in Saudi Arabia: do regional differences matter? Electr. J. 33 (6), 106772.

Mohamed, Zaid, Bodger, Pat S., 2005. A variable asymptote logistic (VAL) model to forecast electricity consumption. Int. J. Comput. Appl. Technol. 22 (2-3), 65–72.

Mujeebu, Muhammad, Abdul, Othman Subhi, Alshamrani, 2016. Prospects of energy conservation and management in buildings–the Saudi Arabian scenario versus global trends. Renew. Sustain. Energy Rev. 58, 1647–1663.

Saudi Green Initiative, xxxx. Saudi Green Initiative Targets. https://www.greeninitiatives.gov.sa/about-sgi/sgi-targets/reducing-emissions/reduce-carbon-emissions/.

SEC, 2023. Saudi Electricity company.

SEEC, 2020. Saudi Energy Efficiency Center.

SERA, 2021. Saudi Electricity Regulatory Authority, Annual Statistical Booklet for Electricity. https://sera.gov.sa/en/.

Shapiro, Samuel Sanford, Wilk, Martin B., 1965. An analysis of variance test for normality (complete samples). Biometrika 52 (3/4), 591–611.

Son, Hyojoo, Kim, Changwan, 2017. Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resour. Conserv. Recycl. 123, 200–207.

Soummane, Salaheddine, Ghersi, Frederic, 2022. Projecting Saudi sectoral electricity demand in 2030 using a computable general equilibrium model. Energy Strategy Rev. 39, 100787.

Tserkezos, E., Dikaios, 1992. Forecasting residential electricity consumption in Greece using monthly and quarterly data. Energy Econ. 14 (3), 226–232.

Worldometer, 2023. World Population. https://www.worldometers.info/world-population/world-population-by.