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Modelling of unsuppressed electrical demand forecasting in Iraq for long term

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  • Mohammed, Nooriya A.

Abstract

One of the main obstacles to Iraq's economic and social development is the lack of reliable electricity supply. In recent years there has been a significant increase in grid-based electricity capacity, but it is still far from being sufficient to meet demand growth. Therefore, it is necessary to build a suitable and flexible forecasting model for this energy system. In this paper, the results of two models are compared to other previous studies of Iraq's energy system to provide the yearly unsuppressed load forecast in the long term. The relationship between the actual load supply and four sets of historical data: population, gross national product, consumer price index and temperature, is examined. The result shows that a reduction in the prediction of electricity demand including suppressed demand occurs when increasing the growth of the consumer price index and removing the war affect. The suppressed consumer demand is estimated by developing a heuristic algorithm and the impact of the reserve margin and load diversity factor is considered to obtain the final forecast. The main contribution of this paper integrates various factors, after rebuilding the lost information, and includes the influence of relevant independent variables, each one for a given weight.

Suggested Citation

  • Mohammed, Nooriya A., 2018. "Modelling of unsuppressed electrical demand forecasting in Iraq for long term," Energy, Elsevier, vol. 162(C), pages 354-363.
    • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:354-363
    DOI: 10.1016/j.energy.2018.08.030
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    References listed on IDEAS

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    1. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    2. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
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    Cited by:

    1. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    2. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
    3. Lee, Juyong & Cho, Youngsang, 2022. "Determinants of reserve margin volatility: A new approach toward managing energy supply and demand," Energy, Elsevier, vol. 252(C).
    4. Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
    5. Mengkun Liang & Renjing Guo & Hongyu Li & Jiaqi Wu & Xiangdong Sun, 2023. "T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting," Energies, MDPI, vol. 16(11), pages 1-27, May.
    6. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
    7. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).

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